{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bOChJSNXtC9g"
   },
   "source": [
    "# Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OLIxEDq6VhvZ"
   },
   "source": [
    "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/logo.png\" width=150>\n",
    "\n",
    "In this lesson, we will explore decision trees and expand on them to build random forests. This type of model, unlike the linear and logistic regression models we've seen so far, have no weights to learn but they offer great interpretability. \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VoMq0eFRvugb"
   },
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qWro5T5qTJJL"
   },
   "source": [
    "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/dtree.jpg\" width=350>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T4Y55tpzIjOa"
   },
   "source": [
    "* **Objective:**  Given some data, choose features and determine conditions to split the data which result in determining the predictions.\n",
    "* **Advantages:**\n",
    "  * Decision tree's can be for classification or regression trees.\n",
    "  * Highly interpretable as you can follow the exact line of though the model used.\n",
    "  * Very little data preparation needed.\n",
    "* **Disadvantages:**\n",
    "  * Poor performance when training data is smaller than number of classes.\n",
    "* **Miscellaneous:** A group of decision trees created a random forest, where the prediction accouts for decisions from all the trees together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Jq65LZJbSpzd"
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-HBPn8zPTQfZ"
   },
   "source": [
    "Let's look at our example decision tree up top which is used to decide whether it's alright to play outside. The data has three features (weather, humiditiy and wind) and the outcome (yes or no).\n",
    "\n",
    "*Steps*: \n",
    "1.   Split the data based on each feature. (ex. Determine yes or no from just the outlook, humidity or wind feature.\n",
    "2.   Calculate the cost for splitting by each feature. Popular algorithms include CART which uses the Gini index or ID3 which uses entropy and information gain to decide on the best split. They all essentially measure the impurity/disorder in the collection of predictions. Check this [blog post](https://medium.com/deep-math-machine-learning-ai/chapter-4-decision-trees-algorithms-b93975f7a1f1) for a detailed breakdown of information gain calculations. \n",
    "\n",
    "  * $ H(X) = \\sum_{c \\in C} -p(c) log_2p(c)$\n",
    "  * where:\n",
    "      * H(X): entropy of entire dataset X\n",
    "      * C: set of classes\n",
    "      * p(c): proportion of # of instances in class c to all instances\n",
    "  \n",
    "  For a binary classification problem, entropy is 0 when all the examples in a split are the same and 1 when half belong to one class (worst case scenario). Once we determine entropy, we need to determine the information gain (IG) that we achieved (ie. how much uncertainty was reduced after we split the data X on the feature F).\n",
    "  \n",
    "  * $ IG(F, X) = H(S) - \\sum_{t\\in T}p(t)H(t) $\n",
    "  * where:\n",
    "      * IG(F. X): information gained by splitting X by feature F\n",
    "      * H(X): entropy of entire dataset X\n",
    "      * T: subsets created by splitting on feature F\n",
    "      * p(t): proportion of # of instances in t to all instances\n",
    "      * H(t): entropy of subset t\n",
    "      \n",
    " **Note**: for regrssion, you can use standard deviation of a group instead of IG.\n",
    "\n",
    "3.   The split with the highest IG is the first feature to split by (which is the root of your tree).\n",
    "4.    Repeat this for all the other features to create branches to our initial feature split. At the end, we will have leaves which will hold mostly samples from the same class.\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wdutxKgxP68B"
   },
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LCcckUIsP9rh"
   },
   "source": [
    "We're going to the load the titanic dataset we looked at in lesson 03_Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B0w9pCHCP-Ep"
   },
   "outputs": [],
   "source": [
    "from argparse import Namespace\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import urllib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nsktUhbXP_GP"
   },
   "outputs": [],
   "source": [
    "# Arguments\n",
    "args = Namespace(\n",
    "    seed=1234,\n",
    "    data_file=\"titanic.csv\",\n",
    "    train_size=0.75,\n",
    "    test_size=0.25,\n",
    "    num_epochs=100,\n",
    "    max_depth=4,\n",
    "    min_samples_leaf=5,\n",
    "    n_estimators=10, # of trees in the forest\n",
    ")\n",
    "\n",
    "# Set seed for reproducability\n",
    "np.random.seed(args.seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kgrunrh2P_JO"
   },
   "outputs": [],
   "source": [
    "# Upload data from GitHub to notebook's local drive\n",
    "url = \"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/data/titanic.csv\"\n",
    "response = urllib.request.urlopen(url)\n",
    "html = response.read()\n",
    "with open(args.data_file, 'wb') as f:\n",
    "    f.write(html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "3eLPo-27P_L3",
    "outputId": "da93034a-432d-4b89-e1b5-c80d902fd962"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass                                             name     sex      age  \\\n",
       "0       1                    Allen, Miss. Elisabeth Walton  female  29.0000   \n",
       "1       1                   Allison, Master. Hudson Trevor    male   0.9167   \n",
       "2       1                     Allison, Miss. Helen Loraine  female   2.0000   \n",
       "3       1             Allison, Mr. Hudson Joshua Creighton    male  30.0000   \n",
       "4       1  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female  25.0000   \n",
       "\n",
       "   sibsp  parch  ticket      fare    cabin embarked  survived  \n",
       "0      0      0   24160  211.3375       B5        S         1  \n",
       "1      1      2  113781  151.5500  C22 C26        S         1  \n",
       "2      1      2  113781  151.5500  C22 C26        S         0  \n",
       "3      1      2  113781  151.5500  C22 C26        S         0  \n",
       "4      1      2  113781  151.5500  C22 C26        S         0  "
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read from CSV to Pandas DataFrame\n",
    "df = pd.read_csv(args.data_file, header=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k-5Y4zLIoE6s"
   },
   "source": [
    "# Scikit-learn implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ql2YltiMS5Sj"
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9ChBporrVYZB"
   },
   "outputs": [],
   "source": [
    "# Preprocessing\n",
    "def preprocess(df):\n",
    "  \n",
    "    # Drop rows with NaN values\n",
    "    df = df.dropna()\n",
    "\n",
    "    # Drop text based features (we'll learn how to use them in later lessons)\n",
    "    features_to_drop = [\"name\", \"cabin\", \"ticket\"]\n",
    "    df = df.drop(features_to_drop, axis=1)\n",
    "\n",
    "    # pclass, sex, and embarked are categorical features\n",
    "    # We need to convert strings to floats for decision trees instead of\n",
    "    # using dummy variables.\n",
    "    df['sex'] = df['sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "    df[\"embarked\"] = df['embarked'].dropna().map( {'S':0, 'C':1, 'Q':2} ).astype(int)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "uvc-WzuvVbUZ",
    "outputId": "86147845-fde0-46d9-e09e-cddee0f71b9b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  sex      age  sibsp  parch      fare  embarked  survived\n",
       "0       1    0  29.0000      0      0  211.3375         0         1\n",
       "1       1    1   0.9167      1      2  151.5500         0         1\n",
       "2       1    0   2.0000      1      2  151.5500         0         0\n",
       "3       1    1  30.0000      1      2  151.5500         0         0\n",
       "4       1    0  25.0000      1      2  151.5500         0         0"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess the dataset\n",
    "df = preprocess(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "KkOqwUtAQHAr",
    "outputId": "2fcadff6-f2d8-4e72-e9f9-a8c623e84d89"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train size: 199, test size: 71\n"
     ]
    }
   ],
   "source": [
    "# Split the data\n",
    "mask = np.random.rand(len(df)) < args.train_size\n",
    "train_df = df[mask]\n",
    "test_df = df[~mask]\n",
    "print (\"Train size: {0}, test size: {1}\".format(len(train_df), len(test_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZezmRsSnTcqr"
   },
   "outputs": [],
   "source": [
    "# Separate X and y\n",
    "X_train = train_df.drop([\"survived\"], axis=1)\n",
    "y_train = train_df[\"survived\"]\n",
    "X_test = test_df.drop([\"survived\"], axis=1)\n",
    "y_test = test_df[\"survived\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZeTRfFhiaO2c"
   },
   "source": [
    "**Note**: Play around with the max_depth and min_samples values below to see the change in performance and decision tree.\n",
    "How do we know when to stop splitting? In cases where we have a large number of features, our decision tree will be very large. If we keep splitting, we could have overfitting. There are a few techniques to tackle this:\n",
    "\n",
    "*  Set a minimum # of training samples on each leaf.\n",
    "*  Set a maximum depth (length of longest path from root to leaf).\n",
    "*  Prune the tree by removing features that add little to no information gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W1MJODStIu8V"
   },
   "outputs": [],
   "source": [
    "# Initialize the model\n",
    "dtree = DecisionTreeClassifier(criterion=\"entropy\", random_state=args.seed, \n",
    "                               max_depth=args.max_depth, \n",
    "                               min_samples_leaf=args.min_samples_leaf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "DIcmEZWxfBOL",
    "outputId": "a7045ea0-f331-4243-ff8f-24ff022c42bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=5, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=1234,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training\n",
    "dtree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_xEuD_lSWVs7"
   },
   "outputs": [],
   "source": [
    "# Predictions\n",
    "pred_train = dtree.predict(X_train)\n",
    "pred_test = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IgLm9MImWz8s"
   },
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dfQX2V_JWksY"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "FV7xOm-iWxjc",
    "outputId": "1b80282c-bf11-4200-e453-e628925d144d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train acc: 0.82, test acc: 0.70\n"
     ]
    }
   ],
   "source": [
    "# Accuracy\n",
    "train_acc = accuracy_score(y_train, pred_train)\n",
    "test_acc = accuracy_score(y_test, pred_test)\n",
    "print (\"train acc: {0:.2f}, test acc: {1:.2f}\".format(train_acc, test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xxOAfpvzWku1",
    "outputId": "d0cdd693-2a73-4d80-a12f-840f10629975"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision: 0.70. recall: 0.79, F1: 0.75\n"
     ]
    }
   ],
   "source": [
    "# Calculate other evaluation metrics \n",
    "precision, recall, F1, _ = precision_recall_fscore_support(y_test, pred_test, average=\"binary\")\n",
    "print (\"precision: {0:.2f}. recall: {1:.2f}, F1: {2:.2f}\".format(precision, recall, F1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PNxTcGWVoSLB"
   },
   "source": [
    "# Interpretability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xqkUvu0-XxcG"
   },
   "outputs": [],
   "source": [
    "# Install necessary packages\n",
    "!apt-get install graphviz\n",
    "!pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j0rwIL2_W82m"
   },
   "outputs": [],
   "source": [
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "Oaz6KdZgoOr7",
    "outputId": "30d3a761-d2a5-4670-9e49-606ab4831e57"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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alqR3xYz0fz8747rW4cTu9TGe49/5Tu3bzOig9+lTKRMDa+Zl3pCu3Lt5zRJ77vAeepZx\nZ9HI2Lc+HtywkJ5l3Nk885ekfqwvJPzADgCKvd8wxrXiHzR6GhOyPd48qdJmjDcmdbr4Y/Ys/gM7\neweKfxCzHhEREZGXRdsMRUREJF5ms5lZ3wYRsmaO1filU0dYNLIHl04dpv5XP8WYF3EsmJW/fcOT\nx48AePzwAftXzuLGpb/4dPSKRNVw7vBulo/uhyn6yT81mYCnW+3Gf16Ps4d2WWKfPH7Eqf1bOB28\nlQ+/HEHpem1jz/drX8tZU48fPWTP0qmcPbSLrhM34ujsQk6fkmR/rzj7V/5JjY7fxNiut3PBBByd\nUlKyTst46+9VzsPqXKu4uKbNQP+lJ+KNi4wIByCT53sxrmXO6w3AtX9i4pKnRDky5i7IvuUzyOnj\nh1e5GhgMRk7u3cjW2aNxz5gNrzLV48xxOfwof4Xtw6dCLVzc08V7TxEREZGkUjNLRERE4hWyejYh\na+aQKY8XNT4bSA4vX+wdUxBx7ACLR/Vk95I/8K3ZjJw+Ja3nrZlL6XptqdCkM6nTZ+HC8QPM/KY9\n4Qd2cOnUYTLn9aFy6x7kKV4u3gPgD21YhF+t5lRs/jkeWXNjNNoBsH3+eM4e2oV7xmx82P0Hchct\nw8N7t9m7bBrrJg9jyU+98S5Xk1QeGWPk863RlMqtviRVukxcOH6QhSO6c/l0GBun/8gHHfoAUObj\nIGZ9G8T+VbPw/7CdZf6Vs8cID9mO/4ftcHZ1e5kfd4I9vHcHAOfUaWJcS/nP2MN7t+PNYzTaEfTz\nEpb+0pc533fCbDJZrnmXr0mdroNxcHKOM8eef87A8qvZPMH1i4iIiCSFmlkiIiISr73LZ2A02tF+\n1AJSe2SyjHsWDaDpwAmMaFaaI1tXxGhm5S9ZiY96jLT8nqtwaSo268aikT24ePJpMyuhcnj70aDX\nLzHeqHdowyIAmn83mRzefgA4uaSiatte3L52md2LpxC2bSWl6ra2mpfdqwQf9x5tyZe7iD+th8xk\neBM/QjcutjSzilT6kOWj+7NzwQSrZtbOBRMBKNPgkwTVP2RrZIKfNeHMz79ifv612EScOMjFE4es\nGlnwdPXdX2H7SJM5x3PnPol6SPDq2aT2yEQB/6qJuq+IiIhIYqmZJSIiIvG6cuYYJlM039d7unXN\njBn+aZY8a5rcvBIRY16e4mVjjKXNmguAR/fvJKqGfH4VYzSy4OlWu5RuaS2NrH97r0w1di+eEutW\nu/wlK8XIlzZLLtJlz8O186ctY3YOjpSu14a1E4cQfmAHnkUDiHpwj+BVs8hfshIZchVI1HO8TE4u\nqQF48K8zyJ55cOemVUxczoftZ3KPRqTJmJ22I+aS06ckRqMdEcdCWPzjV8zo35YULqkoWDr2RlXo\npiXcv32Dis26WVbMiYiIiLwqamaJiIhIvJ6t1onrzKfox1ExxuwdY25NM/CsgZS4lUMpU6d97rX/\n5Xw1/Ou1ZcPUkexcMAHPogHsXzWLh/fuULbhpwnO8SrOzPLI5gk8Pa8qZ6FSVtcunToCQLp/YuKy\nZ9k0zCYTdf8zzKphlad4ORr2GcPPbSuye/GU5zaz9iyZCoBfLW0xFBERkVdPzSwRERGJV/qc+Yg6\ncZ9+i4/j5Br/Sp/EerZCyhSdsGbPv3lk8+SvI3s5H7af7F4lrK4d27EGiL2hc2LPBt5v39tqddb1\ni2e5dv40HllzW8W6ps1AkcofcXDdfO5EXmHnwomky56HAs9p7rwunkUD2DT9R0LWzImxjTJ49WwA\nchcNiDfP/VhWdj3zrJF5/9b1WK9fO3+a8APbyVmoFOlz5Etg5SIiIiJJp2aWiIiIxKtk7ZbMGfQZ\n47rVpWrbr8jh7YuTS2puR17hcngYe5dNJ+Cj9uT1rZCk/M8OMD9zcAf3b10npdvzV2H9f4Ur1eOv\nI3uZ3r8NH3b/gVyF/Xl07w57l09n95Ip2DukwKtszDfxnQ/bz9zBnancsjup0mXi4olDLBzRnegn\njykUWDdGfNmGnxK8ahZzv+/M5dNh1P1iaKzbHp/nVZyZlc+3Iu4ZsxF+YAfLfulDYMv/YGfnwPZ5\nv3N481Jc3DzwKV8r3jxZ8xcmdONiFo/sicFoJJdPKYx2dvwVtp/Fo3r+E1Mk1rl7lk7FbDbjV7PZ\nS302ERERkedRM0tERETiVaJ6E8JDtrFvxUwm92wca0ypOq2SnD9d9jy4pc/Mqf1b+KbG/1ZRDdt+\nM965Zep3IHTTEs6F7mbSlw1jXK/TfXCMNxkCFAqsS/DKWexbPsNqPEPO/AQ2/zxGfLYCRclZqBTH\ndq3FySUVvjVs37yxc3Ckfs8fmdyjEVtmjWbLrNGWawaDgXpf/oCjs4vVnN4VM2KKfmLVXPP/sB17\nlk7j+sWzTOr+cYz7pPLISIVm3WKMm6KfsG/lnzg6paRI5Y/irPXPgZ8QsmaO1djE7g0sP7f47x+x\nNhFFRERE/j+jrQsQERGRN5/BYKBhnzE0+24y+fwq4pzKHTsHR9JmyYV3+Zq0GjyDfH4Vk5zfaLSj\nxaCp5CpcGkenlImaa+fgyCc/LaZq216kz5EPOwdHUqR0JU/xcrQbMY/S9drGOi9XodK0GzmP7F4l\ncEjhhIubByVrtaDjmBUxGkDP+NdrAzw9GypFStfEPeQrUqB0FT4dvZy8vhVIkdIVR6eU5C7iT7uR\n8ylS6cME5XBO5U7XCRso36Qz6XPkw94hBXYOjnhkzY3/R+3pNnkzbukzx5gXtm0ld69fpVBg3Tfm\n8xAREZG3n8Gc2Pc2i4iISLIxZ84cGjVqlKAVTu+K47vWMbF7A2p3HUy5Rh0TNXfpz1+zbc5Yes4O\njnGultjewQ0LmdGvDfrzVkRE5K02V9sMRUREROJhMkVzau8mdi6YRO6iAWpkiYiIiNiQmlkiIiIi\ncVg7cQhrJw2x/F4xlvO0REREROT1UTNLREREJAFSp8tEhaZdKVi6qq1LEREREXmnqZklIiIi75QC\npask6gyxqu16UbVdr1dYkYiIiIgkht5mKCIiIiIiIiIiyYZWZomIiIj8S8SxEH5uF0jVtm/niqwb\nl89zdPtKwrat4nTINqIfR9FuxDwKlK4Sa3z04yg2//kLIavnEnnhDA5OzuQu4s/77XuTJV+hF44X\nERERSSw1s0RERETeIb90qMzd61cTFGuKfsKkHg05uXeTZezJ40eEbVvJid0b6PDTInIX8U9yvIiI\niEhSaJuhiIiIyDskbaYcBNTvQPuR8ylVp1WcsftXzuLk3k24pc9Mm+Gz+W5tBH0XH6NKm6948vgR\n8wZ3wWSKTnK8iIiISFJoZZaIiIjIO6Tz+HWWn8O2r4ozNmzbCgDqdR/BewEfAJAipSvvt/+aS6cP\nc2TLck7v30o+v4pJihcRERFJCjWzREREJMlMpmh2LpjAvhUzuX7hLGbMpMvqSdH3P8a/XlscnJwt\nseEHdrB78WT+OrKPm1cv4JQyFTl8/Ahs/jm5Cpe2ynt81zomdm9A7a6DyZLPh1W/f8fFU6GkTJ2G\ngI86ENjiCwC2z/udHfPGc+PyedJkzsEHHfpQuFK95+bKnMeL1eMHcfHkIRydXfEuV4Pqn/bHxT1d\nvM9qNpvZt3wGe5ZO5dLpI5iio0mfIw+l6rTG/6P2GAyGJH0ub7I71/8GiPWsqyz5CnFky3LCQ7ZZ\nmlOJjRcRERFJCjWzREREJMlW/jaQzTN/thqLOH6AiOMHsHdwIKD+JwDcibzC2E41rOLu3Yrk6PZV\nHN+1jk9+XoJn0YAY+f86spflo/thin4CwK2HD1g5diD2jim4E3mFTTN+ssT+/ddJZgxoS7rseWJt\nppw7vJvlv/a1bHN7/Oghe5ZO5eyhXXSduBFHZ5fnPqfZbGbWt0GErJljNX7p1BEWjezBpVOHqf/V\n/2pJ6OcSl17lPBK8Jc81bQb6Lz2RoNjEcHH3AODiyVDSZMpude3iyVAArp0/neR4ERERkaRQM0tE\nRESS7MjW5Tg6paRx/9/JW6ICdvYOXIs4TfDq2Tg6u1riDAYD+fwCKftxEFnyFcY1bXoe3LlJeMh2\n5gz6jE3TRsXazDq4fgHlGnWkbMPPcHH34Piudcwc0I61k4ZgNplo8PUveJetjsFox4apI9jy569s\nnT2aRn3Hxsh1aMMifGs0pXKrL0mVLhMXjh9k4YjuXD4dxsbpP/JBhz7Pfc6Q1bMJWTOHTHm8qPHZ\nQHJ4+WLvmIKIYwdYPKonu5f8gW/NZuT0KZmoz+VNV6BUZY5uX8WikV9itLMjT7GyPLx3h12LJhO2\n9emWwgd3byU5XkRERCQp1MwSERGRJHNLnwUAr7LVMdo9/bMic14faub1sYpzTZuB6h0HsGn6j8wf\n9jl3b1yzrLYCuBR+JNb8BUpXoXbXwZbfC1Wsg1fZ6oRuWkKtLoMoWauF5VqNzwayZ+lUrpw5Hmuu\n7F4l+Lj3aMt2wNxF/Gk9ZCbDm/gRunFxnM2svctnYDTa0X7UAlJ7ZLKMexYNoOnACYxoVpojW1dY\nmlkJ/VziMmRrZIJjX5WSdVqyf+WfnD8azOQejayulajemP0rZ2EwGpMcLyIiIpIUamaJiIhIktXp\nNpipvVswtGEx8peqTJa8PuTwKUnW/IWt4s6F7mZsl9pEP46KNc/jRw9jHfcsWibG2LPta/9/JZfR\naIdbuizcvX411lz5S1ayOtcKIG2WXKTLniferW9XzhzDZIrm+3reAJgxg9n89Od//n3zSoQlPqGf\ny5vO3iEFQb8uY/2U4Rxcv5Bbf1/ELV1myjfpTKq0Gdi/cpbVeWOJjRcRERFJCjWzREREJMky5/Wh\nx597ORu6h3OhuzlzcCdrJw3Fxc2DZt9OIlMeLwA2ThtF9OMoqrbtRfFqjXBLnxk7hxQYDAaGN/Hj\n3q3YVyHZOzrFHPynIRX7NTCbTS/t+Z4xm57mjOsMq3836hL6ucTlTTgzC8DRKSXVPx1A9U8HWI3P\nH9oNgGwFir5QvIiIiEhiqZklIiIiL8RoZ49n0QDLSqnHDx8wrEkJ5g7uTJcJGwCIvHgW17QZqNqu\nl9XcyAtnuBZxGudU7q+8zhN7NvB++95Wq7OuXzzLtfOn8ciaO8656XPmI+rEffotPo6Ta+oE3S8h\nn0tyFRkRTvDq2RiNdvhUrP3S40VERETiomaWiIiIJNnooPcpUb0xuYsGkDZzLqIfR3Fq/2bu37pu\ntXUwTcbsXD17nB3zx1H8g8YAnA3dxdKfeltWPb1q58P2M3dwZyq37E6qdJm4eOIQC0d0J/rJYwoF\n1o1zbsnaLZkz6DPGdatL1bZfkcPbFyeX1NyOvMLl8DD2LptOwEftyetbAUj45xKXN+HMLIA/ejWl\nZO2W5PQpiZ2DIyf3bmLJT714/OghAfU74J4h6wvFi4iIiCSWmlkiIiKSZBeOH+Tc4T2xXitVp/X/\nfq7XhmO71rJoZE8WjexpGc+avzCZPN/jduSVV10qhQLrErxyFvuWz7Aaz5AzP4HNP49zbonqTQgP\n2ca+FTOZ3LNxrDGl6rSy/JzQz8UW/hz4CSFr5liNTezewPJzi//+YdXcO3dkH0f+eRPhv+X1rUDN\nTt/FGE9svIiIiEhi6XUyIiIikmRdJqwnoH4HMuYqiEMKJ1zcPMhVuDQNvv6F2l2/t8R5l6tBkwHj\nyZzXG4cUTqT2yETpem345Ocl2DukeC215ipUmnYj55Hdq4Sl1pK1WtBxzAocnV3inGswGGjYZwzN\nvptMPr+KOKdyx87BkbRZcuFdviatBs8gn19FS3xCP5fkoM2wWfhUqI1rmvQ4OqUkW4Gi1PvPMNqP\nnI9DipjnliU2XkRERCSxDOZnr+ARERGRt86cOXNo1KgRw7bftHUpNnN81zomdm9A7a6DKdeoo63L\nkVfo4IaFzOjXBv15KyIi8labq5VZIiIiIiIiIiKSbKiZJSIiIiIiIiIiyYaaWSIiIiIiIiIikmzo\nbYYiIiLyVitQuso7fWaYiIiIyNtGK7NERERERERERCTZ0MosEREReW0ijoXwc7tAqrbtRdV2vWxd\nzisRvHo2s74NsvxetuGn1Ok2xPJ738pZiHp4P9a5H/UYSel6ba3GzCYTwUo0TQkAACAASURBVKtn\ns3PhJK5FnMYcHY1H1tz41mxGqbqtsbN3eKF6b1w+z9HtKwnbtorTIduIfhxFuxHzKFC6SoLmz/7v\np+xfOQuA/667gKOzS5LzXz13gh+alrT8nsPLl87j173A04mIiMjbSM0sERERkTfYrO+CCFkz12os\n4vgBIo4f4OiO1bT9YS4GgyHJ+X/pUJm7168mae6pfZsJXjUbBydnHj988NLzi4iIiMRGzSwRERGR\nV6DpwIkUrVI/1mu5Cpfms99WxZvjwolDhKyZi71DCup/9SNeZatjMNpxcu9G5n7fieO71nFq3yby\n+QUmuc60mXJQOLAuXmWqEbppCbuX/JGgeU+iHjJ/+BcUr9aYiydDuXTq8Avnz5Azv+V8s/4f5Ej8\nw4iIiMg7Qc0sERERkTfUlTNHAfCt2YwS1ZtYxgtVrMPl02GsnTSEy+HHXqiZ9e9tfGHb42+wPbN2\n0lAe3btD7a7f83uX2i89v4iIiMjz6AB4ERERsQgP2U7PMu4s+TH286wOb15KzzLubJw28n9zDuzg\nz4EdGNqwGF9XzMDAGnmY3LMxZw/tStA99yydSs8y7oRuXPzca0e2LLcaN5vN7F02ndFB79O3SlZ6\nB2ZiVKsy7Jg/HrPZnIgnfrOlSpsx3pjU6eKPedkunw5jy5+/UveLoaRMnea1319ERETebVqZJSIi\nIhaexcqQPnteglfPpkangdg7pLC6vmfpNIxGO0pUbwrAncgrjO1Uwyrm3q1Ijm5fxfFd6/jk5yV4\nFg14qTWazWZmfRtEyJo5VuOXTh1h0cgeXDp1mPpf/ZSgXL3KeWAyRSco1jVtBvovPZHoemNz9dwJ\nhjYsxs0rEbi4eZC7aAAVm39O1vyFreLylChHxtwF2bd8Bjl9/PAqVwODwcjJvRvZOns07hmz4VWm\n+kupKaHMJhPzhnYjf6nKFKn80Wu9t4iIiAiomSUiIiL/j1+t5qz47RuObFlu1ay49fclTuxeT0H/\nqqROlwkAg8FAPr9Ayn4cRJZ8hXFNm54Hd24SHrKdOYM+Y9O0US+9mRWyejYha+aQKY8XNT4bSA4v\nX+wdUxBx7ACLR/Vk95I/8K3ZjJw+JeNPZiP3b13n/q3rANyOvMzB9Qs4vHkpzb6dhE+F/23ZMxrt\nCPp5CUt/6cuc7zthNpks17zL16RO18E4ODm/1tp3LpzAlTNH+XLGntd6XxEREZFn1MwSERERK741\nmrJq3H/Zs3SaVTNr34oZmEzR+NVuaRlzTZuB6h0HsGn6j8wf9jl3b1zDFP3Ecv1S+JGXXt/e5TMw\nGu1oP2oBqT0yWcY9iwbQdOAERjQrzZGtKxLUzBqyNfKl1xefvL4VKFWnFdkKFsPR2YWr506wacZP\nhG5czNzBXcjnF0iKlK6W+IgTB7l44pBVIwuerkT7K2wfaTK/voPSb/19iVW/f0eNjt/gliHLa7uv\niIiIyL+pmSUiIiJWXNNm4L0yHxC2dQU3Lp8nTabsmM1m9i2fQSqPjLwX8L4l9lzobsZ2qU3046hY\ncz1+9PCl13flzDFMpmi+r+cNgBkz/HNO1rPzsm5eiXjp931ZWg/90+r37O8Vp/l3UxjXtQ6ng7dy\nOngrXmWfbh08H7afyT0akSZjdtqOmEtOn5IYjXZEHAth8Y9fMaN/W1K4pKJg6aqvpfZFI78kUx5v\nSn/Y7rXcT0RERCQ2amaJiIhIDCVrt+TIluXsWz6Dqu16ER6yjcgLZwhs8QVGu//9+bBx2iiiH0dR\ntW0vildrhFv6zNg5pMBgMDC8iR/3bsW/8slgePo+GpPZFOPa40cPYow9W6EU11lXz2uu/X+2OjPr\n/zMYDOQu4s/p4K3cibxiGd+zbBpmk4m6/xlm1bDKU7wcDfuM4ee2Fdm9eMpraWbdv33DchD/V2Vj\nP/S9b5WsAAzZcs3qeyIiIiLyMumvDBEREYmhQOkquGXIwt7l06nSpid7lk4FwK9mc6u4yItncU2b\ngartrN9+GHnhDNciTuOcyj3ee7mmSQfAjYvnYlw7tX9LjLH0OfMRdeI+/RYfx8k1dYKf6U1mNps5\nc3AnAKk8/vd2wvu3bzx/zj9NvWdnb71q5liajSIiIiK2oGaWiIiIxGA02uFboxnrpwzn4IaFhG5a\nimexMqTLnscqLk3G7Fw9e5wd88dR/IPGAJwN3cXSn3rHOOPpeTLmLgjA1jm/kd3blxxevty9fpXt\n8363rAT6t5K1WzJn0GeM61aXqm2/Ioe3L04uqbkdeYXL4WHsXTadgI/ak9e3Qrz3ft1nZm2a/iN3\nb16jSKUP8ciaG3vHFJYzs04Hb8XJNTWexcpa4rPmL0zoxsUsHtkTg9FILp9SGO3s+CtsP4tH9fwn\npshrqd3FzYNh22/Gem1Uq7JcOnWY/667gKOzy2upR0RERN5damaJiIhIrErWasGGP35gwfAveBL1\nkJK1WsSIKVWvDcd2rWXRyJ4sGtnTMp41f2Eyeb7H7X9tmXuetFly4VOhNoc3L+X3zrUs40Y7e4pX\na0zwqllW8SWqNyE8ZBv7Vsxkcs/GseYsVadVQh/ztbp/5yZb/vyVLX/+GuOa0c6e+l/9hJNLKsuY\n/4ft2LN0GtcvnmVS949jzEnlkZEKzbpZjY3pWI2zh3bx+ZStZMlXKN6a/hz4CSFr5liNTezewPJz\ni//+QaHAuvHmsVV+ERERefcYbV2AiIiIvJnSZM5BXt+KPLx7GyfX1BQKrBcjxrtcDZoMGE/mvN44\npHAitUcmStdrwyc/L8HeIUWC7/Vx71/xq9WclG5psXd0IqdPST75aTGeRQNixBoMBhr2GUOz7yaT\nz68izqncsXNwJG2WXHiXr0mrwTPI51fxRR79lanUsjv1/jOc3EX8cXHzwM7eAfeM2Sj+QSO6TNhA\nkUofWsU7p3Kn64QNlG/SmfQ58mHvkAI7B0c8subG/6P2dJu8Gbf0ma3mPFsRpzOrRERE5G1lMD97\n7Y+IiIi8debMmUOjRo2euz1MXr7g1bOZ9W0QTQdOpGiV+q/13maTiW+q5yZ1+sz8Z9pODAbDa73/\ny9T/gxxkyJGfzuPXJXjOwQ0LmdGvDfrzVkRE5K02VyuzRERERF6BmQPa0bOMO0t+6hV/8EtyOfwo\nD+7eIrDFF8mykXX13Al6lnGnZxl3Ht69betyRERE5A2lZpaIiIjIW+LsoZ2kyZT9ta8IExEREXmd\ndJiCiIiIyEtU/INGFP+gkU3u7f9Re/w/am+Te78MGXLm15ZYERERiZdWZomIiIiIiIiISLKhZpaI\niIiIiIiIiCQbamaJiIjISxNxLISeZdxZO3GIrUuROLxJ/532rZhJzzLuhG5cbOtSREREJJlQM0tE\nRERERERERJINHQAvIiIi8o7JVrCYDloXERGRZEsrs0REREREREREJNnQyiwRERFJELPZzP6Vf7J3\n2TQunT6CKTqaDDnzU6pua/xqNsNo9/w/K8IP7GD34sn8dWQfN69ewCllKnL4+BHY/HNyFS5tFWsy\nRbNzwQT2rZjJ9QtnMWMmXVZPir7/Mf712uLg5JyouNctoXXtWTqVeUO60uK/f1AosK5VjmfXWg2e\ngXf5mgAc37WOid0bULvrYLLmL8yaCYOIOH6QDDnyUavLIMZ2rknZjz+lzucxz8E6vHkpU3u3oPqn\n/Qls8R8ijoXwc7tAqrbtRdV2vQgP2Z6o+fD0+7Bv+Qz2LJ1q+T6kz5GHUnVa4/9RewwGg1WOB3dv\nsXrcfwndtIQHd26RKXdBqrbr9VI+cxEREXm3qJklIiIi8TKbzcwc0I6D6xdYjUccCyHiWAhpM+ck\nn1/FWOfeibzC2E41rMbu3Yrk6PZVHN+1jk9+XoJn0QDLtZW/DWTzzJ+t73P8ABHHD2Dv4EBA/U8S\nFReXXuU8MJmi440DcE2bgf5LT8Qb9zLqisu5w7tZProfpugnAJjNJjyLlSF99rwEr55NjU4DsXdI\nYTVnz9JpGI12lKjeNNaciZ1vNpuZ9W0QIWvmWMVdOnWERSN7cOnUYep/9ZNl/EnUQ37vXIuLJ0Mt\nYxHHDzDlqyYUrvRh0j8MEREReSepmSUiIiLx2rt8OgfXLyClW1qqfdKPggHvkzJ1Gq6ePc6uRZOx\ns3d47lyDwUA+v0DKfhxElnyFcU2bngd3bhIesp05gz5j07RRVs2sI1uX4+iUksb9fydviQrY2Ttw\nLeI0watn4+jsmui41+1V13VowyL8ajWnYvPP8ciaG6PRDgC/Ws1Z8ds3HNmynCKVP7LE3/r7Eid2\nr6egf1VSp8v03LyJmR+yejYha+aQKY8XNT4bSA4vX+wdUxBx7ACLR/Vk95I/8K3ZjJw+JQHYPm8c\nF0+Gkj5HPj7s/gM5vH25e/Mam2f+ws4FE174MxEREZF3i5pZIiIiEq/9K2YC0GzgJKsVWNkKFqNB\nr2JxznVNm4HqHQewafqPzB/2OXdvXLOsKgK4FH7EKt4tfRYAvMpWt2xdzJzXh5p5fZIUF5chWyMT\nHJtQL6OuuOTw9qNBr19ibOPzrdGUVeP+y56l06yaUftWzMBkisavdss48yZm/t7lMzAa7Wg/agGp\nPf7XIPMsGkDTgRMY0aw0R7ausDSzQjcuwWAw0PL7aWTMXRCAtM4ufNj9B/7+6ySn9m1O+gciIiIi\n7xw1s0RERN5iTk5OADx5/CjG1rHEuHruJM6p3J+7lTAu50J3M7ZLbaIfR8V6/fGjh1a/1+k2mKm9\nWzC0YTHyl6pMlrw+5PApSdb8hZMU97q96rry+VWM0ciCp03D98p8QNjWFdy4fJ40mbJbzrVK5ZGR\n9wLejzNvYuZfOXMMkyma7+t5A2DGDGbz05//+ffNKxGW+GsXwkmdPrOlkfVvBUpVeWnNrCePHuJk\no7PSRERE5PVRM0tEROQt5uHhAcC9m9dxS5/ZJjVsnDaK6MdRVG3bi+LVGuGWPjN2DikwGAwMb+LH\nvVvWq6My5/Whx597ORu6h3OhuzlzcCdrJw3Fxc2DZt9OIlMer0TFxeVVnJmV0LoMhqcvlTaZTTFy\nPH704Ln5U6ZO+9xrJWu35MiW5exbPuOfg923EXnhDIEtvojzgP7EzjebntYc12f3vOblq3Tv1nXc\n0z7/8xEREZG3g5pZIiIib7GCBZ+uhLl8+sgLNbMy5MzHmYM7ObVvM3l9KyRqbuTFs7imzRDjzXWR\nF85wLeI0zqncY8wx2tnjWTTAcpbW44cPGNakBHMHd6bLhA2JjnvdElKXa5p0ANy4eC7G/FP7tyTp\nvgVKV8EtQxb2Lp9OlTY92bN0KgB+NZu/1Pnpc+Yj6sR9+i0+jpNr6njzpsvqyfmj+7ly5liM1VnH\nd69LUG0JcSU8DK/33ntp+UREROTNpGaWiIjIW8zDw4M8+fJzOngrBUpXSXKeEjWacubgTmYMaEe1\noH4ULF0V59TuXD13gt2LJlPs/YZ4FisT69w0GbNz9exxdswfR/EPGgNwNnQXS3/qbVnh82+jg96n\nRPXG5C4aQNrMuYh+HMWp/Zu5f+u61ZbEhMbF5VWcmZXQup41dbbO+Y3s3r7k8PLl7vWrbJ/3O0e2\nLE/SvY1GO3xrNGP9lOEc3LCQ0E1L8SxWhnTZ87zU+SVrt2TOoM8Y160uVdt+RQ5vX5xcUnM78gqX\nw8PYu2w6AR+1tzQ+CwXW4a+wfUzt3YIPvxxBDq8SlgPgX+Z5WWdCtvJp27jPBhMREZHkT80sERGR\nt1zd2rWYPmch1Tt+E+tZSwnhV6MZx3etI3TjYuYP7RbjeuFKHz53bql6bTi2ay2LRvZk0cielvGs\n+QuTyfM9bkdesYq/cPwg5w7viT1XndaJjnvdElpX2iy58KlQm8Obl/J751qWcaOdPcWrNSZ41awk\n3b9krRZs+OMHFgz/gidRDylZq8VLn1+iehPCQ7axb8VMJvdsHGueUnVaWX4u0+ATQtbM5eLJUMZ1\nrWMZNxgMFKn8EQfXL0hUjbE5fzSYaxf/onbt2i+cS0RERN5sRlsXICIiIq9W27ZtuRpxhuO71iY5\nh8FopPl3U6j/1U/k8PbD0SklTi6pyO5VggZf/0Ke4mWfO9e7XA2aDBhP5rzeOKRwIrVHJkrXa8Mn\nPy+J9VD6LhPWE1C/AxlzFcQhhRMubh7kKlyaBl//Qu2u3yc67nVLTF0f9/4Vv1rNSemWFntHJ3L6\nlOSTnxZbticmRZrMOcjrW5GHd2/j5JqaQoH1Xvp8g8FAwz5jaPbdZPL5VcQ5lTt2Do6kzZIL7/I1\naTV4htXLAuwdnQj6dRn+H7XHNW0G7B2dyJq/MC0Hz3ihFYP/tmvhBLy8fShZsuRLySciIiJvLoP5\n2StnRERE5K1Vu3Ydgo+epuvkLQk6CFwkObl4MpSf21XkjylTaN48YeeDiYiISLI1V80sERGRd8Dp\n06fx9vaheqdvCaj/ia3LEXmpxnWphYdjNDt3bE/yVloRERFJNuZqm6GIiMg7IE+ePHzxxeesHT+I\nq+dO2LockZdm29yxhB/Ywehff1EjS0RE5B2hZpaIiMg7YsCAAfh4ezGlx8fcu3nN1uWIvLATu9ez\n/Je+DBo0iBIlSti6HBEREXlNtM1QRETkHXL16lX8SpbC3j0zrYfPwcklla1LEkmS82H7mfBFPRrW\nr8+UKZNtXY6IiIi8PtpmKCIi8i7JkCEDK5Yv496Vs4zt+AE3Lv1l65JEEi1042LGdalNhXLlGDfu\nd1uXIyIiIq+ZmlkiIiLvGG9vb/bu2U06V0dGf1KZYzvX2LokkQR5EvWQNRO+Z3q/1nTo0I5lS5fg\n6Oho67JERETkNdM2QxERkXfU3bt36dDhE2bN+hPvMtWo2WUQ6bLnsXVZIrE6vHkZK0f35d7Na4z4\nYTiffvqprUsSERER25irZpaIiMg7btOmTXTu0pXjx47hVa4Gxas1Jp9vRRycnG1dmrzjbl29SNi2\nFexbNpWIE6E0bdqMYcOGkiVLFluXJiIiIrajZpaIiIjAkydPmDVrFr+N/Z1dO3dgMNqRKVc+UqXL\njGPK5HFIfPSTx9jZO9i6jDeS2RQNGDAY3/wTJszR0Ty8e5PIiHCuX7lAypQuNGhQny5duuDr62vr\n8kRERMT21MwSERERa1euXGHTpk0cPHiQK1eucOfOHVuXFC+TycTatWvJmTMnBQsWtHU5b5z169eT\nIUMGChUqZOtS4mU0GnF3d8fT05PixYtTtmxZnJycbF2WiIiIvDnUzBIREZHkr1+/fowaNYpDhw7h\n6elp63LeOL/99htdu3Zl165dlChRwtbliIiIiLwINbNEREQkeTt06BC+vr6MHDmSzp0727qcN5LZ\nbKZq1ar8/fff7Nu3DwcHbccUERGRZEvNLBEREUm+njx5gr+/P/b29mzfvh1jMjgTylbOnDlDoUKF\n+Prrr+nTp4+tyxERERFJqrn6i09ERESSrREjRhAaGsrEiRPVyIpH7ty56d+/P9999x1hYWG2LkdE\nREQkybQyS0RERJKlkydPUqRIEfr168fXX39t63KShejoaPz9/bGzs2Pbtm3Y2dnZuiQRERGRxNLK\nLBERkbfdzZs3MRgMCf6ncePGti45XiaTifbt25M/f36+/PJLW5eTbNjZ2TFx4kSCg4MZM2ZMguac\nOnUqzu+Lj49PouvYt28fBoOBb775JtFzRURERNTMEhERkWRn7Nix7Nixg4kTJ+ow80QqVKgQX331\nFb169SI8PNzW5YiIiIgkmppZIiIibzl3d3fMZrPVPydPngSgQoUKMa7NmjXLxhXH7eLFi/Tp04ce\nPXpQokQJW5eTLPXt25fcuXPToUMHEnriROXKlWN8V8xmM4cPH37F1YqIiIhYUzNLREREkpWOHTuS\nIUMG+vfvb+tSki1HR0cmTpzI5s2b+eOPP2xdjoiIiEiiqJklIiIiFqtWrcJgMPDjjz+yefNmKlSo\nQKpUqfD19QVgwoQJGAwG5s2bF2Pus2uLFi2yGjebzUyaNImAgABSpUqFs7MzRYoUYfTo0QleFfTM\ntGnTWLZsGRMmTMDJySnpDyqUKlWKLl268Pnnn3PhwoUXzrdlyxaaNWtG3rx5SZEiBenTp6d27dps\n3749QfOjo6P55ZdfKFGiBGnSpMHd3R1fX19GjhzJ/fv3rWJf5ndKREREkh97WxcgIiIib54dO3bQ\no0cPnjx5Ajw9cD0pzGYzLVq0YMaMGVbjhw4donPnzhw8eJBx48YlKNe1a9fo3r07n332GeXKlUtS\nPWJt0KBBLF26lI4dO7JkyZIk57l8+TIVKlSwGrt27RrLli1j1apVrF+/nvLly8eZ4+uvv2b48OFW\nY/v372f//v04OjrSuXNn4OV+p0RERCR50sosERERiWHu3Lm0bNmS48eP8+TJE4KDg5OUZ/r06cyY\nMYNChQqxYsUKIiMjuXv3Lps3b6ZIkSKMHz+enTt3JihXp06dcHZ25vvvv09SLRJTypQpGT9+PMuW\nLWP+/Plxxq5fvz7Wtxlu27YNg8FA1apVWbp0KefPnycqKoorV64wZ84cUqRIwZAhQ+KtZdGiRbi4\nuDB//nxu3rzJvXv3OHDgAF9++SWurq6WuJf5nRIREZHkSSuzREREJIbSpUtbtg2+iMmTJ2NnZ8fq\n1avJnDmzZbx8+fLMnDkTb29vFi9ejL+/f5x5li1bxpw5c1i5ciWpUqV6oZrEWmBgIG3btqVz584E\nBgaSNm3aROfImDEjgwcPZujQoQQFBXH16lXLqj6A0NDQeHNky5YNgDp16mBv//RP1CJFilCkSBGr\nuJf1nRIREZHkSyuzREREJIYqVaq8cCML4MiRI0RHR5M9e3bs7e2xs7PDaDRiNBrx9vYG4K+//ooz\nx61bt+jYsSOtWrWiWrVqL1yTxDRixAjs7Ozo3r37c2Oe9zbDsmXLsmPHDgICApg7dy4XL160amQB\nPHjwIN4aRo0ahclkIm/evAQFBTFmzBhCQkJixL2M75SIiIgkb2pmiYiISAweHh6xjhuNT/90iO0M\nrdgaFs/ioqOjiY6OxmQyWZogz0RFRcVZS/fu3Xn06FGM85Tk5XFzc2Ps2LH88ccfrFq1KtHzhwwZ\nQlRUFAMGDODUqVM8ePDA8t+6QIECCcpRpEgRjh07xtSpU8mdOzdbt26lWrVqeHt7W63sehnfKRER\nEUnetM1QREREEixDhgwAnDlzJsa1DRs2xBgrWLAgwcHBXLx4ETc3t0Tfb+PGjUyaNIk5c+aQPn36\nxBcsCVarVi0+/vhjgoKCOHz4cKK2c4aHh5MxY0a++eYbq/HTp09z8uRJ0qRJk6A89vb2lC9f3nJY\n/P379ylQoADt2rVjz549wIt/p0RERCT508osERERSTAvLy8AfvzxRzZt2sSDBw84e/Ys3bt3Z9Gi\nRTHi27Vrx/3796lSpQrLli3j77//JioqinPnzrF8+XLq16/P+vXrY73X/fv36dChA7Vq1aJBgwav\n9LnkqdGjR/PgwQP69OmTqHk5cuTg6tWr/Prrr9y6dYtbt26xYsUKatSokeA3YQYEBDB27FjCwsJ4\n8OABt27dYtWqVURGRhIeHm6Je5HvlIiIiLwdtDJLREREEszT05OPPvqIBQsWEBgYaBm3t7enRYsW\nTJs2zSq+VatWbN68mSlTplC7du1Yc3bo0CHW8b59+3Lt2jV+++23l/cAEqd06dIxYsQIWrduTcOG\nDSlbtmyC5gUFBbFy5Uq6dOlCly5dLOPFihXDx8eHS5cuxZsjODj4uW8h/Pd35EW+UyIiIvJ20Mos\nERERSZSJEyfStm1bPDw8cHJywt/fn3Xr1lm2hv2bwWBg8uTJzJ49mypVqpAmTRocHR3x9PSkXr16\nLFy4kCpVqsSYt3v3bn7++WdGjRpF1qxZX8djyT9atGhBrVq1aN++PQ8fPkzQnLp16zJjxgwKFy6M\ns7MzmTNnJigoiPXr15MiRYoE5di9ezedOnXCy8sLZ2dn0qVLR5kyZZgwYQKjRo2yxCX1OyUiIiJv\nD4P536dlioiIiNhYVFQUxYsXJ2PGjKxbt+6lvFVREufChQt4e3vTqVMnBg0aZOtyRERERP5trlZm\niYiIyBtl0KBBnDlzhnHjxqmRZSNZs2bl+++/Z9iwYezfv9/W5YiIiIhY0cosEREReWOEhobi6+vL\n8OHD6dq1q63LeaeZTCYCAwO5desWe/fuxcHBwdYliYiIiADMVTNLRERE3gjR0dH4+/tjNBrZvn07\ndnZ2ti7pnXfixAmKFi1Kv379+Prrr21djoiIiAhom6GIiIi8KUaOHMmhQ4eYOHGiGllviPz589O/\nf38GDhxIWFiYrcsRERERAbTNUERERN4AZ86coVChQnz99df06dPH1uXIvzx58gR/f38cHBzYtm0b\nRqP+X6iIiIjYlLYZioiIiG2ZzWaqVq3K33//zb59+3Q20xvo0KFD+Pr6MnLkSDp37mzrckREROTd\npm2GIiIiYlu///47mzdvZuLEiWpkvaEKFy5Mz5496dWrF+Hh4bYuR0RERN5xWpklIiIiNnPx4kW8\nvb0JCgpiyJAhti5H4vDo0SNKlChBpkyZWLt2LQaDwdYl/R979x1e4/kGcPx7crL3EJEYIYKQiE2M\n2JTWnq1ZOlCz1k+ptmirNUprlyqtGVvsvRIkIlaWPUNClux1zu+PVDhOECGJcX+uK1fjGfd7nzfn\ntHL3eZ5XCCGEEO8n2WYohBBCiMLToUMHQkJCOHPmDEZGRoWdjniBkydPUr9+ff766y/69u1b2OkI\nIYQQ4v0k2wyFEEIIUThWrlyJt7c3ixcvlkLWW6JOnToMHjyYkSNHEhERUdjpCCGEEOI9JSuzhBBC\nCFHgHjx4gKurK126dGHevHmFnY54CUlJSbi7u1O9enW8vLwKOx0hhBBCvH9kZZYQQgghCt7QoUPR\n09Pj559/LuxUxEsyNjbmzz//ZP369WzcuLGw0xFCCCHEe0iKWUIIx1k+JwAAIABJREFUIYTIF+np\n6axbt47U1FSN9u3bt7NmzRrmz5+PhYVFIWUnXkXTpk359NNPGTx4MDExMRp9N27c4MCBA4WUmRBC\nCCHeB1LMEkIIIUS+2LVrF926daNy5cqcOHECgIcPHzJw4EB69+5Nu3btCjlD8SpmzZqFUqlk9OjR\nAKhUKubMmUPFihVp06YNGRkZhZyhEEIIId5VuoWdgBBCCCHeTUeOHEFPT4+rV69Sv359hg4dysOH\nD0lJSWHmzJmFnZ54RRYWFixYsID27dtTt25dlixZgr+/PyqVCoCAgADq1KlTyFkKIYQQ4l0kxSwh\nhBBC5It9+/aRnp6e/ed58+ZhbGzM8OHDsbW1LcTMxOvSunVr6tWrx6BBg1AoFNmFLD09PY4cOSLF\nLCGEEELkC9lmKIQQQojXLiEhgfPnz2u0ZWRkkJiYyJQpU+jSpQtRUVGFlJ14Hc6dO0fNmjU5ceIE\nGRkZGoXLzMxMDh06VHjJCSGEEOKdJsUsIYQQQrx2Pj4+ZGZmarU/atuyZQsuLi54e3sXdGriFaWn\np/PNN99QvXp1goODc/w5q1Qqjhw5kr1SSwghhBDidZJilhBCCCFeu8OHD6Ovr//M/oyMDB48eMC4\nceMKMCvxOoSFhTFt2jRUKpXGaqynJSQkcO7cuQLMTAghhBDvCylmCSGEEOK1279/P2lpac/sVyqV\n1K1bl3379hVgVuJ1cHNzY//+/VhbW6Onp/fMcY/OzRJCCCGEeN2kmCWEEEKI1yo5OZnAwMAc+xQK\nBQBfffUVhw8fxt7eviBTE69J48aNOXv2LNWqVUOpVOY4RqVSyblZQgghhMgXUswSQgghxGt1/Pjx\nHLef6erqYmhoiJeXF3/88cdzV/WIN1/x4sXx8fFh9OjRwONC5SOZmZkcPHgQtVpdGOkJIYQQ4h0m\nxSwhhBBCvFZHjhzROi9LV1cXR0dHTp06RdeuXQspM/G66erq8ssvv7Bq1SoMDQ21CpSxsbGEhIQU\nUnZCCCGEeFdJMUsIIYQQr9WBAwc0VmYpFAq6du3K2bNnqVSpUiFmJvLLJ598QmBgIGXKlEFXVze7\nXVdXV87NEkIIIcRrJ8UsIYQQQrw2aWlp+Pn5oVarUSqV6OnpMWvWLFatWoWJiUlhpyfyUYUKFQgI\nCKBjx47ZWw7VajWHDx8u5MyEEEII8a6RYpYQQgghXhs/Pz9SU1PR0dHBzs4OHx8fhg8fXthpiQJi\namqKl5cXv/32G0qlkszMTPbv31/YaQkhhBDiHaNQy6mcQggh3iApKSkcO3aMgIAArl27RmxsLCqV\nqrDTErkUGhrK+fPnsbOzw8PDQ+vsrMKko6ODpaUlTk5OVK9enQYNGmBoaFjYaeVZREQEhw4d4uzZ\ns0RERBAfH1/YKWl48OABvr6+pKam8uGHH8rKvLeIoaEhVlZWVKpUCQ8PD6pUqVLYKQkhhBBPWifF\nLCGEEG8Ef39//vhjDhs2biQ5KRFT2+IYFS2NjrElKGQh8dsi7WEUGUlxGBcrAyheOL5AqVWokmJJ\njrxOwv07GBmb0LlTJ4YPH0bNmjULO7tcycjIYM2aNSxcuIDjx0+gVCqp4OxE8WJFMTV984pFqalp\nhF25SsVyZeXplW+R1NQ0omJiCQq9yMP4BEqVLEn/zz5j0KBBFC1atLDTE0IIIaSYJYQQonCFh4cz\nZuz/WL1qJZZlKmPXsAe21VpiYG1f2KmJd1hq9F3uB+4h4sgqYq+d55MePZk+7VccHBwKO7VnOnTo\nEMOGDSU0NIx2rZrRq0t7mjSoi7HR27u6TLzZ1Go1p88FsXHbbpav3Uhqejrfffc9Q4cOleKkEEKI\nwiTFLCGEEIVn4cKFjBw9Bl0zG8p0/56iNVsXdkriPRR5aifX1k4iIz6K32ZMZ+DAgYWdkoaEhAS+\n/PILVq9ew0ctmjD9h3E4l3Es7LTEeyYpOYXpc//ktwVLcSztyJo1a2X7oRBCiMIixSwhhBAFLzMz\nk6+//pq5c+dSuv0IyrQbjo6eQWGnJd5jqvRUrm39netbZjN4yBBmz5qFUqks7LS4desW7dq2JfzO\nbRbP+pnWzRoVdkriPXfj1h2+HDUB/zPnWbVqNW3bti3slIQQQrx/pJglhBCiYKWlpdG+Q0f2HzhI\nxS//oGjtNoWdkhDZIv22EfLnMJo2bcLWzZsK9QD7oKAgWrRojrWFOZuXL8CxZPFCy0WIJ6WnZzBs\n/GSWrdnAnDlzGDRoUGGnJIQQ4v0ixSwhhBAFq++n/VizbgNVx67FvGy1wk5HCC0PrwRyZlp3Pu7a\nmeXL/i6UHCIjI6lTpzbFixZhy7+LMDczLZQ8hHien2cvYPKMOWzevFlWaAkhhChI63QLOwMhhBDv\nj6lTp7Li339xH/F3gRSyVOmpXNkwjUi/baRE3UGtyqTJ4ssoDd+8p77lt9iwk4Qf9SLukh/J92+h\n1DfErHQVSn3wOUWqtdAaf/DzsmSmJuUYy6Xfr5Ro2idfrvu0oEXDuHtsHUCB/ezMy1bDdcifrJjZ\nm4ouFRg3bly+X/NJKSkpdOjQAVQqvP6aI4WsAnLzdjjb9h5k+54DHPb1Iy09He+Vi/mgiafW2D8W\nL2f091OfG+/nb0cz+qvPnzsmL3GsnKuRmJSc49i5v/zAl30+fm6812n8iEGE34ugR49POHbMR87Q\nEkIIUWCkmCWEEKJABAQEMOHbbynfc1Kuihivw3XvP7ixfX6BXOtNlhRxjVM/dtBoU6WnEh10hOig\nI5Tv8QOlWg94464bHXSUuz7rUeobkZmW8y/v+cWmcmPK9fie8RMm0KJFC2rUqFFg1540aRIhwUEc\n9V6DrY11gV33fVf/o65E3I96bfGa1Pd4o+Lkl1lTvuXS1et8/HF3zp07L085FEIIUSBkm6EQQoh8\np1araeDZiLAHqVSbsBkUigK57skJzUl+cIta32/D2L4sCoVOgVz3TZMceYPQZeOwb9AVc6eqGNoU\nJzUuktv7lnNj+zx0dPVpOP8CukZm2XMOfl4WM0dXak7cWqDXfUSVnsqJb5pgUb4WCTeCiL8ZVCir\n6s5M7YyTmZoTx31QFMD79sqVK7i6ujLtu7EM6tcz368nHmvQpjs1qrjxUYsmbNq+h79Wej1zZdaz\nJCYlU6pqA0qVcCDwgHeec3leHCvnalRxrcihLavyHP91ux1+FzfPD5ny44+MHDmysNMRQgjx7lv3\nfv6tXgghRIFauXIlJ4774tz75wIrZAGkRIdjbF8WE4dy70whKz0hhogTWwheMhK/71rlao5RUUeq\njV1NsXqdMC7mhI6eAUZFSlLu42+xcqmLKiONxPBLrz3XV7nu1U0zyUiOp3yPSa89r5dRtsdkTvn7\nsXLlygK53tdfj8C5jCNf9C64rWLvmqiYWNZt3cGXIydQt3WXXM87tm0tv/80kZaNG6Cvn7fVRV5b\nthOfkMinH3fO0/zXHaeglHCw5+uB/Zg8eRKRkZGFnY4QQoj3gGwzFEIIke9+/HkqxRp0wczRtUCu\nd3HFd9zcvRjIKv7s620PQPGmvanYbxoAMaEnuHPwX+IunyY1+g5KIzMsnGtQus1QLMvX0ogXde4g\ngdN7UL7nZMxKu3F1wzQeXj+PiX1Zak/enTVIrSb8yBruHFpFwu0Q1JkZGBcrS/GmvSjZ7NM8F/HU\nmenEXjpF9PnDRJ0/RPz186jVKnSNzSlao3WeYj5Jocz6pV3fvMgrx3pd1024FcKNHQtxGzgHPVPL\nAs3raWaOrhRr0IWfpv5Cr1698vVaQUFBeHtvY8u/i9DVVebrtXJy9IQ/i/9di1/gOW7fuYu5mSl1\nalRhzJAvqVerutb4mLiHfPfLLDbv2EPcwwQqVXBm4qihREXH8PnX37Dmz9/p1OaD7PFqtZrlazey\ndOU6LoReJCMjk3JOpfm8VzcGftojzyvf0tMzOH4qkH2Hj7H3sA+B54NRqVRYmpvTrnWzPN+PvPhr\n5Tr09HTp0bndGxGnII0Z8iWL/13LggUL+P777ws7HSGEEO84KWYJIYTIVydPniQsJJjan8wq7FSy\npcVFEvBTR402VXw0DwL3EnXuINXHrcPKRfucmrhL/lxaMxl1ZgYAapUqq0Ot5sLCIdzz3agxPuFW\nMGHLx5NwM4iK/WfkOr+ku1eIOn+IqAuHiQnxJTMlEYVSF3OnapTpOBKbyo0wd6qGQidvBQ+1WkVa\nbCThR9YQHXQEG/cmGNmW0hqXGH4Zn1F1SYm6g76ZNZYuHpRuMwQzR7d8u65arSJk6Rhs3Btj59E+\nT9d53Uo074ffd63w8/Ojdu3a+XadpUuXUraMI62aNsy3azzLvcgHNOvUW6PtQXQM2/ceYvfBo+z2\nWoanx+Mib0pqKi279OVsUEh22+lzQXT6dBBd2moXWdVqNZ8OHcvqjZpb5s6HhDF8whTOBYexYPrk\nXOd78co19h72Yd/hYxz29SMhMQldXSW1qroz4euvaNG4AbWqVkapLLiiYFDoJfxOn6V96xavdNZZ\nbuKEXb6KS72W3LoTThFrKxrUqcWYIZ9T1a1Snq/7qoyNDOnbvRN/L10qxSwhhBD5TopZQggh8tW2\nbdswsyuFeRn3Artm+V6TKd9rMocGVMDE3plaP2x/aoQCa7dGlGr5GWaObuhbFCE9MY6Y0OME/zmC\n69vm5FjMivDzxqHRJ5RuMwSjoo7ZxaS7vhu457sR05IVKdf9W8zLVkdHT5+H185x8d9vuXNwJQ4N\nP8bCueYLc/cZWYfk+zeBrG169vU6Y125MdaV6qNrbP5K9yUx/DLH//f4/B8dPQNKNP+Uct2/zXF8\nekIM6QkxAKTGRhBxYguR/juoPGQhRWt+mC/Xvb1vGQm3w6j365Fcx89v5mWqYFa0JN7e3vlazNrm\n7U2nD1sWyNlcT1MoFDRvWI/Bn/WhqpsLRYsUISYujqPH/fn862+YPnexRjFr3l8rOBsUQvmyZZgz\n9XtqVXMnKjqG2Yv+Zt7SFVrxV23YyuqN3rhVLM/PE0ZTu3oVDPT1OX3uAl9/+xN/rfSi78ed8KhR\n9YW5lvdozvWbtwEo41iSHp3b0aJRfRrX98DCXPv8tYLy10ovgFfeGpibOFExsUTFxAJwN+I+67bu\nYPPOPaxcMIsOHxbMAzZy0vGjlkyft5hz587h7l5w/84XQgjx/pFilhBCiHx1zOc4ZhXqFXYaGvQt\nbHHuNp4b2+YS8vdY0h4+yF5tBVnb3HJi4VyDSp/N1NoyGH5kDQodJdXGrsbA0i673crFA7ev5nN8\nXCPuB+zOVTGrIKnSU4m7dIqEW8FYlNPcWmnt6knxxj0xd6qC0sCYxPDLXN8+j0i/bYQsGYmNW0OU\nhqav9bqpMfe4su4XynWfgIG1/Su9ttfNzKU+Pr4n8i1+VFQUFy9douEPY/PtGs9jZ2vDj+NHMX3e\nYr4ae5r7UVFkZGRm958PuagxfuP23SgUCryWzKFSBWcATE2MmfXjt4Rdvsq+I74a45ev3YhSqWT7\nqr+wt7PNbvf0qMW/82dQpXEbvHftz1Ux602UkprKqg1bsbezpVXT3B8Yn5c4TRrU5bOe3ahRxQ0T\nYyPCLl9lxvwlbNy2mwGjJtCsYT3MTAv2QQmP1KjihrmZKcePH5dilhBCiHwlxSwhhBD5KjgkGMum\nAwo7DQ1xl/wJ+Lkzqoz0HPtVaSk5tlu7euZ49lXi7TDUqkyODa+R1aBWo0ad/T1AStSdXOVW/7eT\nJIZfJvrCIaLOH+au7wZuH/gHhY4Sc6dq2FRuhLVbIyzKVkOhfLn/jJs4ONP837uoVZmkxd3nwdn9\nXFo1iYBfulH3l8MaW/6qfL1MY665U1Xch/xJwC9diQn2ITrYB9vqH5Abub1u6PJvMC3pQolmfV/q\ndRUE0xIuBB/8M9/ih4RkFVDdXMrl2zWe5/ipQFp07kNaes6fiZQUzc/E1es3cShml13IelKLxp5a\nxazgsMtkZmbiVLMxkLXt8NEDtR/98+ad8FzlevHEPsIuX/1vm6EPqzZs5c9/1qBUKqlVtTItGjeg\nWcN61K5WpcDOHtu0fQ/RsXGM/urzV9ramJs4G5fN1/hzzaqVWb1oNh90+5RDPic57HuSNi2b5jmH\nV6FQKKhY3pnQ0NBCub4QQoj3hxSzhBBC5KuY6GhsC/hw8Re57j0XVUY6Th1HYd+gCwZW9ujo6oNC\nge/YBqTHR+c4T8805/Nr1Oqss7PUqswc+wFUGWm5zs/EwRkTB2dKtvwcVUY6cZf8iTp/mOgLh7i2\neRZXN81E18gM2+of4DpwTq7jPqLQUWJgVYzijXuiSk8l7J8JRPp54/jR4BdMVGBZvjYxwT6kxd1/\nrddNT4jlfsAuAPb1cchx/sEvsgonzZbdeulC3qvSM7chOioq3+JH/Re7yCuctfQqps35k7T0dCaO\nGkLPzu1wsLfDQF8fhUKBm2droqJjtOY8azvko+LUk1T/nS+Xmfnsz8izCmk5qeDsRAVnJ4Z81pu0\n9HSO+z8+AP6nWfOZMnMu5mamtP2gGX//8Wuu4+bV0lXrAPj0k1fbYpjXOAqFgvq1a3DI5yT3Ih+8\nUg6vqoi1dfb7WQghhMgvUswSQgiRr9LTUtHRzdtj7vNLUuQN9C1sceo0WqM9OfI6yfeuoWti8VLx\nTOydiU89j+ecM698rtXTdHT1sKpYD6uK9aDbN6QnxBB94TBR5w8/czvky1ClZxXZMpLjXzxYrSb2\noh+QtVXztV73v4Lgm0pHV5/0tNR8i5+amhXbQF8/367xPNdu3sLO1oaJo4ZotF+9fpPL125gZaH5\nvnYqXYpTZ84TcvEyFctrrs7af8RHK34FZyeSzgdxI/Doaz/XSl9Pj0b1atOoXm2mfDOSqJhY9h/x\nYe8hHy6EXnxxgFd0+doNjhz3p27NapQvW6ZQ4qjVanz8AgAoVrRw/+eBoaG+1ko+IYQQ4nWTYpYQ\nQoj3jmGR4iSGX+TW3qXY1+8CQOwlfy6u+C57ldXLcGjcg+A/R3D6l244dRyFuXN1dI3MSYuNIOF2\nKOGHV1Oi+adZ2xRfkZ6pFXYeHbDz6JDrOde2/k5GUjx2tdtgVNQRpYExqbERPAjcw9UN0wCwrPD4\nwPvr2+aSHh+FXe12GNmVRkdXn8S7l7mxbR4xwT7oGptnFdde43X1zKxp/u/dHOOcnNCc+JtBNFl8\nGaVh4ZwF9K4rVdyBkItXmP/3Snp2bgeAr/9pRn8/NXtV1ZM6ftQS/8BzdP9iOHOmfk/NqpWJio7h\nj8XL2XtYu5jV75Mu+PgF0Kp7P74dOYTa1d2xMDfjXsR9LoReZNmaDQz8tAdNG9R95ddiY2VJt/Yf\n0a39R68cKzeWrlyHWq2m78ed8j3O9HmLeRAVTZe2rXEqXQoDff3sM7MO+ZzEwtyMhnXz7yEFQggh\nxJtCillCCCHeOyWa9ibq7AHC/plA2D8TstvNHN0wLeFCamzES8VzaNCN2JDjhB9dy5nf+uQ4pniT\nXrmKtb9vieduV3ySvoUtDeeee+G4jMRYbuxYyI3t83LsL1avEzaVGz8xPi5r/I6FWmMVSl0q9p+O\nrpHm6poD/RxRqzJptvx2nq8rCs8Xvbuz68ARRkyYwogJU7Lbq7pVwtWlHPciNLeVDu7fi9UbvDkf\nEkaLLo/f8wqFgs5tW7HBexd6eo//mtm7WweOnPDjn7Wb6Nh3YI45fNazW65yNSrp+tztik+ys7Xh\n1lnt4lpO+g4Zw+qN3hptbXt+kf39mj9/p1MbzXPiMjIy+XfdJkyMjeja7vlP+DQr7U5GZibJt4K0\n+nIbJzb2IbMW/s2shX9r9enqKlkwbTLmZnl7MIMQQgjxNpFilhBCiPeObfVWuA2ax/Vt80iKuIqe\nsQVFqrfEudsEAn/N3S/UGhQKKn05G5sqTblzcCXx18+SmZqEgbUDpiUr4uDZ/bWsysqrMu2/xsjW\nkYiTW0kMv0R6Yhx6ppaYla6MQ4Ou2NVprzG+dLthGNg4EHFiC4nhl8hIisfAsihWLh6Uaj0QM0e3\nfLmuKDxtP2jGP/NmMH3uYi5fu4GlhRltWjblx/GjaN29n9Z4I0ND9m74h4lTf2PLzn08jE+gUgVn\nJo4ayulzF9gAWD6xNVGhULBk1lRaNWnIXyvXcfrcBRKTkinuUIzKFSvQp1sHmnm+WU89zY1tew4Q\ncT+K3t06vNITBHMb53/DBlDCoRjrtu4k9NIV4uLjKVbUFk+PWowY8ClV3SrlOQchhBDibaJQ53RK\npxBCCPGaKBQKKg9ZhF2ddoWdihB5FnFyK+fnDsjxcPPXwcvLi+7du5MW/nY/BU6lUuHRqgtng0II\nv3AcGyvLwk5JFLBPBoxAx9AMLy+vwk5FCCHEu2udTmFnIIQQQggh3j5jJ/3Kqg1buXk7nKTkFM4G\nhfDJgBGcuRBMo3q1pZAlhBBCiHwj2wyFEEIIIcRLu3jlGrMXaZ/dZGpizPQfxhVCRkIIIYR4X0gx\nSwghhBBCvLQZk76hiI0VPicDuB1+D3MzUzzr1mLiyCG4upQr7PSEEEII8Q6TYpYQQgghhHhpzmUc\nWTJramGnIYQQQoj3kJyZJYQQQgghhBBCCCHeGrIySwghhBC5FnvRj6sbZ/LwaiBqtQrz0pUp3W4E\nNpUb5T5G2EnCj3oRd8mP5Pu3UOobYla6CqU++Jwi1VpojU95cJv7gXt4ELiHmBBfVBnpVBuzChv3\nJs++iFpN+JE13Dm0isQ7YSh09bBwrkHpjwZjWaFOXl66EC/Fxy+AH2fOw//MOVQqFdUquzJu+EBa\nNKqf6xhp6en8tmApqzd6c/X6TYyNDKlfpybfjxlKFdeKrzQ+MSmZrbv24bVlB+dDwrgXcR8ba0vq\n16nJ2CFfUNWt0ivfAyGEECK/KNT59YxpIYQQAlAoFFQesgi7Ou0KOxUgqxhzakp7ynb5H2Xajyjs\ndN4qUecPcWZGL9SqTM2ORz/j2m1fGCMp4hq+o+s9s798jx8o1XqARtuRIe6kxd3XaHteMUudmc65\nOV9yP2CXVp9CR0mz5bdfmOfTIk5u5fzcAeTXX5u8vLzo3r07aeGh+RI/L3z8AmjSoSc/jB3O+BGD\nCjudt8qeQ8do33sAmZmanxWFQsGqhbPo3LbVC2NkZGTStucX7D/qq9VnoK/PzrVLaVCnZp7Hf//r\nbKb+vjDHa+vp6bJx2QI+aOL5wjyf9smAEegYmuHl5fXSc4UQQohcWifbDIUQQgjxQqqMdEKWjkGt\nyqRU6wE0mh9E40VhlO3yP1CrCV02jsyUxBfGUSh0sKncGLdB86g33YemS69Tf5Yfjh8NBuCy189k\nJMdrzDEqUpKSzftRbexqijfp+cJrXN30G/cDdqFvXgTXgXNptDCUpn9do/r/1mBV8dmFNCFeh7T0\ndL4a+x2ZmZmMGNCP8AvHiQz144exw1Gr1Qz9ZhIJiUkvjLNi/Wb2H/XFoZgdm/9ZSNTFAG4EHuHb\nkYNJTUtjwKhvNYplLzve1NSEHp3bsWn5Qi6e2Efc1TP4791E84b1SE/PYNj4yflyf4QQQojXQbYZ\nCiGEEOKFooOOkPLgNlYuHpTv8UN2e5n2I4i/fp7IUzu4f3o3xep1em4co6KOVBu7WrOtSEnKffwt\nD6+cJib0OInhl7AoWz27v9YP27O/f3B6z3PjpyfGcWPnQhQ6SqqNWYVZ6crZfdZujbB2y/12SCHy\nYv8RX27eDsfToxbTvv9fdvv4EYM4cyGYzTv24r17P590ev5KRu/dBwCYM/U7PmzeGAAzUxO+Gz2U\nc8FhbN21j0O+J2nmWS9P48cM/kLrmlVcK7L+7/mUqd6QazduERUTi42V5SvdDyGEECI/yMosIYQQ\nbx61mvDDq/Gf1JaDXzhzoH9pToxvxq19f8NT27yizh1kX297bu5aTNzlAAJ+6sTBz5w4PKgiQYuG\nkZ4Qmz322pbZnJrSHoAr639lX2/77K+nY8WEHifgp44c/MIZv+8+yI6hSkvh6qaZHP+fJwf6OXLo\ny/KcntqVqPOHtF7Gk/Gig47iP6ktBz4rw5Gv3Aj5azRp8VHZY+Mun2Jfb3vClo/P8ZZEnNzKvt72\n3NixIM+39VXEhp4AoFi9zlp9xep3ASAmVHt708tQKPUA0DcvkucYD87sQ5WWgm31lhqFrHeVWq1m\n2ZoNNGz7MdblqmNepgo1mrVnwd8rtbZE7j54FH0HF/5YvJyTAWdp3rk3lmWrUqxSHfoPH0d0bFz2\n2J9nL6BJh6xVcD9M+x19B5fsr6djHTnuT7NOvbEuVx2PVo/fH8kpKUyZOZfKDT/ErLQ7RSrU5INu\nn7Ln0DGt1/FkvAPHjtOw7cdYOFWleOV6DBw9kftR0dljTwScQd/BheETpuR4T9Zv3Ym+gwu/LVia\n9xv7Co6dOAWQY7GqZ+es7dZHjvu/ME7kgwcAOZ6NVcU16+dwxNcvz+OfxdjIkJLFHdDVVWJibPTC\n8UIIIURhkJVZQggh3ixqNRcWDuGe70aN5oRbwYQtH0/CzSAq9p+hNe3htTNcXvsjqow0ADLTkrl7\nbB0pD25RY8Kml0oh7pI/l9ZMRp2ZkZWSSgVkbbU7/Ws3Yi8+/kVUlZFGdPAxokN8cPn0F0o07ZNz\nvNWTss+aSktL4c6hlcRePEntSbtQGppg4VwTc6eqhB9bh3P3CSgNTTRi3N63DKWBMcUbv3ib3f6+\nJbTPtXoGfQtbGs4998JxSRHXADAtUUGrz6xUxf/GXM/VNZ+kVqtIi40k/MgaooOOYOPeBCPbUi8d\n55H462cBsHFvSsSJzVzd9BvJkdcxsCyGbc1WOHUcja6xeZ7jv0nUajWfDh3L6o3eGu3nQ8IYPmEK\n54LDWDBde6vYqTPnmfDTTFLTsj4rSckprFi3mRu37rB/478vlcPxU4GMmzKNjIys95tKlVVAS0tP\np3X3/vj6n84em5qWxsFjJzjkc5I5U7/nyz4f5xjvf5OnZW9kAw0yAAAgAElEQVSHS05JYemqdfj4\nBXB853pMTYzxqFGVmlUrs2LdZn4aPwpTE2ONGAuXr8LE2Ij+Pbu8MH+jkq5a51o9i52tDbfO+rxw\n3OXrNwBwdSmn1Ve5Ytbn58p/Y56niLUVAGeDQihVwkGj72xQ1tlql67dyPP4Z7l45RoXQi/SrlUz\nDA0MXjheCCGEKAyyMksIIcQb5a7vBu75bsS0ZEWqjV5JowUhNFlyhRoTNmFWypU7B1cSd/mU1rx7\nvhtxaPgx9Wb40vSva9ScuBVDm+LEhJ4g/mYQkLUlrubELQCU7fI/mv97N/vrSRF+3tg36Eq96T40\nW36bOj/uBeD23qXEXvTH0KY4VUf+Q+M/L9JgdgBOnUajQMHFFd+RFheplVuEnzf29btQb4YvTZZc\noea3mzEtWZHE8Mtc3zY3e1zJlp+TmZLAXZ/1GvMT71wkJvQ49g26FlohJiM5AQBdEyutvkdtGUkP\ncx0vMfwy+3rbs79PcY4Oq8a1LbMp0fxT3IcufqU80x5mrXaLuxzA+XmDSAy/hCojneQHt7i5azH+\nk9uRmZLwStd4U6zasJXVG71xq1ierSv+5F7wSWIuB7J/47+4V3Lhr5VenAg4ozVvzaZt9P24EyG+\ne4i7eoZDW1ZRsrg9R0/4cy44q+gxfsQgDm5eCcAPY4eTFh6a/fWkDd676NWlA0HHdpF8Kwi/PVlF\n6PlLV+Drf5qSxe3ZtHwhD8JOcfXUQSaOGoJCoWD091O5F/lAK7cN3rvo2aUdIb57iLkcyIFNK3Cr\nWJ6wy1eZMe/xe2PIZ72JT0hk5fotGvNDLl7myHF/enXtgKV54XxW4uOz3l/WlhZafVb/bdmLexiv\n1fe0lo2zDl8fNn4KO/YdIiExibsRkUyeMQfv3fuz4sQ9zPP4nCQmJdP7q1FYmJky/ftxL8xRCCGE\nKCyyMksIIcQbJfzImqzzjsauxsDSLrvdysUDt6/mc3xcI+4H7MbCuabGPJvKjXDp92v2ny3L18Kx\nzeD/VnMFY1bKNdc5WDjXoNJnM0Gh0GiP8MtaAVN5yCIsnGsAoGtkhlPHUaTG3OPOwRXcP72H4k16\nacwzL1uNSl/Myo5nWaEOVUb8je9YTyL9t2Udog7Y1WnHpdWTub1vGSWa9c2ef3vfMgBKtvwsV/nn\n5Wl9L/S8p/i9hif8qdJTibt0ioRbwViUq5X3QP/lEn50LaU++IJSrQegZ2pF3OUAQpeNI/FOGDd2\nLsKp46hXzrmwLV+7EaVSyfZVf2FvZ5vd7ulRi3/nz6BK4zZ479qPR42qGvNaNKrP3F9+yP5zvVrV\nGf3V51mruYJCca/kkusc6tSowqKZP6J46rOyYVvWkyRXLZxNnRpVADA3M2XiqCHcjYhkyQovtu05\nwOe9umnMq1XNncW//Zwdr0GdmmxYOg+3hq3ZsG03P4wdDkCXdq0ZN2UaC5evYkDfT7LnL1yedR7b\n4P6an8FnSb4VlOvXmlvP/6jk/rPSv2dX/l23mVNnztOhz0CNvl5dO7Bi3WZ0dHTyPP5piUnJdP70\nK8IuX8V75WIcSxbPda5CCCFEQZOVWUIIId4oibfDUKsyOTa8Bvv7lmB/n+Ls6+PAvj4OHB+XdXh3\nStQdrXk5PaXO2NYRgIyXXIlj7eqpVciCrK12eqZW2YWsJxWp1iJ7zNNs3BppxTMq6ohxMSeSIh5v\n+9HR1aNEsz4k3A4l5r8zqjJTErnrsx6byo0wcdDetlRQdI3NAMhIjNHqy0iK/W9M7lfCmDg40/zf\nuzRbfhvPPwKp+NkMkiNvEPBLN5Lv38x7nkZZeVqWq0X5XpMxtCmO0sAYa1dPXAf8DsCDs/vzHP9N\nEhx2mczMTJxqNsaopCuGJSphULwiBsUrUqVxGwBu3gnXmtewXh2ttjKOJQGIT3jxEymf1MyznlYh\nC+DKtZvYWFlmF7Ke9FGLJlljcthq16JRfa14ZRxLUs6pNFdvPH5f6Ovp8UXvjwkKvcTRE1nbfhMS\nk1i5fgstGtXHpVzZl3odr5O5uSmAxhlkj8T+12ZhbvbCOAb6+uxd/w9jh3xJGceS6OvpUbpUCX7/\naSJt/ruHRWys8zz+STFxD2ndvR8nT59h64rFeHq8QkFZCCGEKACyMksIIcQbRa3OOp/qeWc+PToX\n60k6ejkcVPzol+KXXDmkZ5rzL3waMfNJiaZ9uL71d27vX4aViwd3fdaTkRxPyQ+0nzz2LPlxZpax\nXRkAEm6Haa2cir8Z8t+Y0rnO8RGFjhIDq2IUb9wTVXoqYf9MINLPG8ePBr90LABj+6wihpmjm1af\nmWPWgfDpD6O0+t5Gqv/OcnvemU9p6elabUaG2ucgPSogvewaO+vnPOkupyLX6/Rln0/49Y9FLFy2\nGk+PWqxcv4WH8QkM+Vz73LpnyY8zs5xLZxXRg0IvUbdmNY2+8yFhAJT9b8yLmBgb8eP4kfw4fqRG\n+6Ax3wFQ3d31lcYD3I24z0c9PuP6zdt4r1xMgzo1tcYIIYQQbxpZmSWEEOKNYmLvjFLfiMaLwjTO\ntHryy33YkjzHz/6l/b/D3V+GsV0Z0uOjeXglUKsv6sz+7DFafRcOaxXUkiNvkHTvKsZ2mr/U6lvY\nYlenPZH+O0iLi+T2/uUYF3OiiHvTl873dbJ08QDgnu8Grb57/53xZVmh7itdQ5WeVaTMSH7xeULP\n8miFXvyNC1p98TfOA1n3+F1QwdkJYyND7of6a5xp9eTX2sV/5Dn+oy1puS32PKlsmVI8iI7BP1C7\nULpz/+GsMTkUdPYe9tHainftxi0uXb2Ok6PmgwHsbG3o2v5DNu/cw73IByz6ZzXOZRxp1bThS+f7\nOjXwyCoGPX0wP8DKDVsB8PTIe8HoyvWbrNqwFaVSSccPW7zS+KvXb9Ko/SfcvB3O9tV/SSFLCCHE\nW0OKWUIIId4oDo17kJmWzOlfuvEgcC9p8VGoMtJJeXCbB2f2ce73z4gOOprn+I8OK48NO0l6gvaW\nueexq90WgPNzB/Dg7H4ykuNJjb7Ltc2zuHNwBTq6+thWb6k17+GVQIKXjCQp4hqZqUnEXvTj7Oz+\nqDPTKVqrjdb4kh98gToznaDFX5NwK4SSLfq/1IqwZstvP7MQ+PRXblZlAVi7Nsw+UP/iqh9Ij48m\nI+kh17bMJvLUDvTMrClao9UL41zb+juX1vzIw6tnSE+IQZWeSvL9m9zas4SrG6YBYFnBI9ev9Wlm\njm6YO1Ul9qIfF1d8R0rUHTJTk4gOPkbQoqzzlnL6Gb2N+n3ShaTkFFp178f2vYe4HxVNWno6N2+H\ns2PfIbp9PpQDx47nOf6jA8yPnTxFVEzsS83t3CbrvdBj4Nfs3H+Yh/EJ3Ll7j59mzWfJCi8M9PVp\n01K7QOsfeI4vR07gyvWbJCYl4+MXQJf+Q0hPz6Bzmw+0xg/9vA/p6Rl8OXI8F0IuMviz3i+1Iiz5\nVtAzC4FPf+VmVRZAs4b1sg/UHzvpVx5ExxD3MJ6fZy9g8469FLG2ol2r5rmK1bnfYLbvPURUTCzx\nCYls2bmXll37kpySwpd9PqaEg32exweFXqJxhx7Exj5k55qlWqvIhBBCiDeZbDMUQgjxRnFo0I3Y\nkOOEH13Lmd9y3i709AHrL8O4WBkMrIoRHXyMw4MqZbc//UTDnJRo0Z8I/+3EXfLnzAztHCr0noK+\nRVGt9qK123D32DrCj6zRaDdxcKZ0myFa483LuGNRrhZRZw+ga2SGQ8PuuXlp+UpHV4+K/adzZmZv\nbu5cxM2dix53KhS49J2K0tBEY86Bfo6oVZkaB9JnJMZyY8dCbmyfl+N1itXrhE3lxhptFxYM5p7v\nRo22wOk9sr93H7qYorUfFwUrfjaDU1M6cHP3Ym7u1nw6onmZKi+1ZfNN1rtbB46c8OOftZvo2Hdg\njmM+69ktx/bccC7jiEMxOw4eO4G96+MC49NPNMzJV/17sWn7Ho6fCqR97wFa/TMnj6dY0SJa7Z3a\nfMCK9VtYvlbz513B2YnRg7V/btXdXalbsxq7DhzB3MyUPt065ual5St9PT3mT5tMhz4Dmb3ob2Yv\n+ju7T6FQ8MfP32FqYqwxx6y0OxmZmVoH0vudPpP9JMInNW1Ql18mjtFqf5nxfyxenv1Eyfof5fw+\n8d+7iSquFZ/xSoUQQojCIyuzhBBCvFkUCip9OZvKQxZh7doQPRMLdHT1MCrqiG2NVlQZ8XfWAe15\nDa+jxH3YEizL10ZpYPziCU/Q0dWjxjgvnDqOwti+LDq6eigNTbGqVJ9qY1ZRomnOxTfLcrWpNmYl\n5mWroaNviJ6ZNQ6NelDj281aBaBHSjTLiuXQ6GOUhqYv9yLziY17E2pM2Ii1qydKQ1OUBsZYVqhD\ntTGrsavTLlcxyrT/Gpe+U7FyqYu+eREUSj30LWyxqdKUyoMX4DZw7ivnaVbKlTqTd2FXuy16plYo\nlHoY25WhTLvh1JiwEaV+DuervYUUCgVLZk1l1cJZNPOsh5WFOfp6epRxLEm7Vs1Zv3QuzTy1H4yQ\nW0qlkrWL/6B+7RqYGL/cPdPX02OX199MHDWE8mXLoK+nh5mpCY3r18F75WK+7PNxjvPq1aqO98rF\n1KrmjpGhIUWsrej3SRcObFqhVQB65FGsTz/ujJlpzp+ngvZBE0/2bfiHpg3qYmZqgomxEQ3q1GTb\nqiV0adc613E2LV9Ihw9bULSIDSbGRlR3d2X2TxPZtmoJRoaGrzxeCCGEeFsp1C/zjGAhhBDiJSkU\nCioPWZTrYse7JOrcQQKn96B8z8mUavVyq4EurvyOm7uXUH+GL0ZFS+dPgiLXIk5u5fzcAVrnOb0u\nXl5edO/ePVernt5Fuw8epW3PL5gx6RuGfdH3peaO/n4qc5b8Q4jPbpxKl3rxBJGvPhkwAh1DM7y8\nvAo7FSGEEO+udbLNUAghhHiDqFWZRAcd5fa+5VhV8JBClhDPkJmZyf6jx1m0fDWeHjWlkCWEEEK8\nR6SYJYQQQrwhrm6cwdVNM7P/nNN5WkIImDJzLlNmPt6SmtN5WkIIIYR4d0kxSwghhHjDGFgVw/HD\nQdhU0X7amxDiMXu7oowc1J9WTRsWdipCCCGEKEBSzBJCCCHyiY17k1w9JfERp06jceo0Oh8zEuLN\n9EETz5c6L2ziqCFMHCUrF4UQQoj3lTzNUAghhBBCCCGEEEK8NWRllhBCCFFIHl47i993rXDqOOqd\nXJGV8uA29wP38CBwDzEhvqgy0qk2ZhU27k2eOSf2oh9XN87k4dVA1GoV5qUrU7rdCGwqN9Iae/Dz\nsmSmJuUYx6Xfr5Ro2ue1vRbxZgo4e4G6rbu8syu10tLT+W3BUlZv9Obq9ZsYGxlSv05Nvh8zlCqu\nFV84v//wcaxYtxmA6EunMTUxzu+UhRBCiAIhxSwhhBBC5Au/Hz4kLe5+rsdHnT/EmRm9UKsys9ti\nQk8QE/YJlYcswq522/xIU4g3UkZGJu17DWD/Ud/sttS0NLbtOcDeQ8fYuXYpDerUfOb8A8eOs3L9\nFoyNDElKTimIlIUQQogCI8UsIYQQQuQLoyIlsavVhiLVWxLpv407B1c+c6wqI52QpWNQqzIp1XoA\nZdoOQ6HU5dbepVxZ/yuhy8ZRxL0pSkMTjXmW5WtRc+LW/H4pQhS4Fes3s/+oLw7F7Jg/bRKeHrVI\nSExk8b9r+fG3eQwY9S3nDm9HqVRqzU1JTWXw2O/p1bU9Zy+Eci449+eRCSGEEG8DOTNLCCGEEPmi\n1g/bqdD3Z2wqN0ZHqf/csdFBR0h5cBsrFw/K9/gBPTNrdI3NKdN+BEVrfkh6fDT3T+8uoMyFKHze\nuw8AMGfqd3zYvDFmpibY2xXlu9FDadeqOZeuXueQ78kc5/44cx7xCYlM/+GbgkxZCCGEKDBSzBJC\nCPFWUKsyubXnL05ObMmhARU4NKACft99wM2di8hMS9YYGxN6ggsLBuMzqi4H+pXi8FeunPmtD7EX\n/bXiRp07yL7e9tzctZiYYB/8J7Xl4GdOHB1enevec7LH3drzF75j6nOgnyO+YxsQ4ef93FjRQUfx\nn9SWA5+V4chXboT8NZq0+Khcvlg14YdXZ+XyhTMH+pfmxPhm3Nr3N6jVeb4vb7LY0BMAFKvXWauv\nWP0uAMSE+mr1idzLzMxk3tIV1PmgE0VdamPrUguPVp2ZvehvrW1oR0/402fwaFzqtcTUsTIObnXp\n2Hcgvv6nteLuPngUfQcX/li8nEM+J2nY9mMsy1alTI3GTJv7Z/a4eUtX4NqgFWal3XHzbM0G713P\njXXg2HEatv0YC6eqFK9cj4GjJ3I/KjpXr1WtVrNszQYatv0Y63LVMS9ThRrN2rPg75Won/oMvcx9\nKUiRDx4A5Hg2VhVXFwCO+Ppp9V0IucisRUuZ9eMErC0t8jdJIYQQopDINkMhhBBvhcteP3Nj+3yN\ntofXzvHw2jkUunqUbNEfgLS4SAJ+6qgxThUfzYPAvUSdO0j1ceuwcvHQih93JYBLayajzswAIDM6\nmcteP6OjZ0BqbCQ3ts/LHpt09woX5g7EeIoTZo6u2rEu+XNp9aTss5/S0lK4c2glsRdPUnvSLq2t\nchrUai4sHMI9340azQm3gglbPp6Em0FU7D/jpe/L8+zvW0LjnKrn0bewpeHcc7ka+zKSIq4BYFqi\nglafWamK/425rtWXGH4Zn1F1SYm6g76ZNZYuHpRuMwQzR7fXnuPb7tupvzFz/l8abafPBXH6XBD6\n+vp81a8nAPciH9CsU2+NcQ+iY9i+9xC7Dx5lt9cyPD1qacU/GXCWcVOmkZGR9V5KSr7Htz//hoG+\nPhGRD5gxf0n22ItXrtFz0EicnRxzLNYcPxXI/yZPIzMzK1ZySgpLV63Dxy+A4zvXP/cgc7VazadD\nx7J6o2bB+XxIGMMnTOFccBgLpk9+6fvyPEYlXbNzfRE7WxtunfV54bgi1lYAnA0KoVQJB42+s0FZ\n2wYvXbuh0a5SqRg0diItGzega7sPc5WPEEII8TaSlVlCCCHeCvcDdqE0MMZ9+F80XhRGk7+uUuen\nfTh+OOip4pACa7dGVB35D56/n6bZsps0nHeeykP/REdXn+vb5uQYP+LEFkq26EeDWf40WXIF92FL\nUCj1uLppJrf3L6fS5zNpOP8CjRaE4PjhQNRqFTd3Lco5lp839vW7UG+GL02WXKHmt5sxLVmRxPDL\nXN8297mv867vBu75bsS0ZEWqjV5JowUhNFlyhRoTNmFWypU7B1cSd/lUHu7Lmy0jOQEAXRMrrb5H\nbRlJD7X60hNiSI68jjozndTYCCJObMHv+w+JPLUjfxN+C23ZuQ8TYyO8lszhfqg/sVfOcGrfZkYO\n6o+p8ePikEKhoHnDemxavpBrAYdIvHGB2+d8WL1oNgb6+kyfuzjH+Ou27uCrfr247HeAmMuBrF38\nB3p6uvz42zwW/bOaRTN/5M55X+4Fn+Trgf1QqVT8/ufyHGNt8N5Fzy7tCPHdQ8zlQA5sWoFbxfKE\nXb7KjHk5X/+RVRu2snqjN24Vy7N1xZ/cCz5JzOVA9m/8F/dKLvy10osTAWde+r4UtJaNPQEYNn4K\nO/YdIiExibsRkUyeMQfv3fsBiIvT/EwsXL6a4LDLzP3lh4JOVwghhChQsjJLCCHEW8HA2h4A22ot\nUSiz/vNlVsoVs1KaK6P0LWxx7jaeG9vmEvL3WNIePshebQWQcCskx/g27k0o3/Pxao2itT7CtnpL\nIv23U77HDzg06pHd59z9W+4cWkXinYs5xjIvW41KX8wChQIAywp1qDLib3zHehLpv42yXf73zNcZ\nfmQNCh0l1cauxsDSLrvdysUDt6/mc3xcI+4H7MbCueZL3Zfnabb8dq7H5puntn7lps/a1ZPijXti\n7lQFpYFxVrFw+zwi/bYRsmQkNm4NURqa5lPCb58SDsUAaNOyKbq6WYeGu1dywb2Si8Y4O1sbfhw/\niunzFvPV2NPcj4rKXm0FcD4k5/f9B008mTHp8RlNHT9qSZuWTdm0fQ/Tvv8f/T7pkt3384TRLF21\nnpCLl3OMVauaO4t/+xnFf5+hBnVqsmHpPNwatmbDtt38MHb4M1/n8rUbUSqVbF/1F/Z2ttntnh61\n+Hf+DKo0boP3rv141Kj6UvfleZJvBeV6bG7179mVf9dt5tSZ83ToM1Cjr1fXDqxYtxkdncf/Xzr8\nXgTf/TKLn8aPorh9sdeejxBCCPEmkWKWEEKIt0L5npM49/vn+Iyui03lxpiVcsWiXE2t7WRxl/wJ\n+Lkzqoz0HOOo0nI+A8fKpa5Wm2GREgBYPrUtUaGjxMCqGGkP7+cYy8atUXYh6xGjoo4YF3Mi6d7V\nnF/gfxJvh6FWZXJseI2sBrUaNers7wFSou5kj8/tfXnT6RqbAZCRGKPVl5EU+98Yc432Kl8v0/iz\nuVNV3If8ScAvXYkJ9iE62Afb6h/kT8JvoRmTvqHb58NwqdeClo0b4F7JBY+aVanqVklj3PFTgbTo\n3Ie09Jw/QykpOX+GPOtqbz10LFE8q++pbYlKpRKHYnZE3M/5HLkWjepnF7IeKeNYknJOpbl09XqO\ncx4JDrtMZmYmTjUbA1nbDh+dk/XonzfvhGePz+19KWgG+vrsXf8PU2cvYJ33Tu6E38PB3o6vB/TD\nzrYIK9ZtpoiNdfb4YeOn4OZSngF9PynErIUQQoiCIcUsIYQQbwWzUq7Um3aU2EuniLvkT0zYSa5u\nmomemTWVBy/EtGTWuTvXveeiykjHqeMo7Bt0wcDKHh1dfVAo8B3bgPT4nA+Q1tEz1Gp79Mu0jp5B\nDjMUqFWq1/b6HlGrs2I+7wwrVUZa9ve5vS/P8yacmWVsVwaAhNthWJTTLHzE3wz5b0zpFwdSKLAs\nX5uYYB/S4nIuNr6v3Cu5cOHITo6fOs3xU4EcOxnAj7/Nw8baipULfsOtYnkAps35k7T0dCaOGkLP\nzu1wsLfDQF8fhUKBm2droqK1C44Ahgban5NH9ahn9any4TP0KObzzrB6slCX2/vyPPlxZhaAibER\nP44fyY/jR2q0DxrzHQDV3bNWYEbHxrF11z4ADIrn/Jm3LlcdgKSbQdkr0IQQQoi3lRSzhBBCvDUU\nSl2sXDyyD3DPTEvGd0x9gpeMpPaknQAkRd5A38IWp06jNeYmR14n+d41dE3y/+leURcOU7bzWI3V\nWcmRN0i6dxVjO8fnzjWxdyY+9Tyec85orUR6ltzclzedpYsHbJvLPd8NFG/SS6Pvns/6rDEVtFfP\naVGrib2Y9YQ3fQvbFwx+/+jqKvH0qJW9UiopOQXXBq34ctQEfHesA+DazVvY2dowcdQQjblXr9/k\n8rUbWFnk7n35KvYe9uH7McM0Vmddu3GLS1ev4+RY6rlzKzg7kXQ+iBuBR7EwN8vV9XJzX94UV67f\nZNWGrSiVSjp+2ALIn6KgEEII8SaTYpYQQoi3gv+ktth7dsWqggdGtqVQZaQRHXyM9IQYja2DhkWK\nkxh+kVt7l2JfP+uMnthL/lxc8V32qqf89vBKIMFLRlK63TAMLO2Iv3GB0GXfoM5Mp2itNs+d69C4\nB8F/juD0L91w6jgKc+fq6BqZkxYbQcLtUMIPr6ZE80+xds06HDq39+V53oQzs6xdG2JoU5yY0BNc\nXPUDZdoOQ6HU5dbepUSe2oGemTVFa7TKHn9921zS46Owq90OI7vS6Ojqk3j3Mje2zSMm2AddY3Os\nKtYrxFf05mnY9mN6de2Ap0dNSpcqQVpaOgePnSA6JlZj62Cp4g6EXLzC/L9X0rNzOwB8/U8z+vup\nBVY08Q88x5cjJzBu+ECKFbXlzIVghn0zmfT0DDq3ef7W0X6fdMHHL4BW3fvx7cgh1K7ujoW5Gfci\n7nMh9CLL1mxg4Kc9aNogqzia2/vyPPlxZhZA536D6d+jKx41q6Kvp8eBo76M/O5nklNSGNSvJyUc\nss7MK2JtRVp4aI4xajbvwLngUKIvnX7uUyCFEEKIt4kUs4QQQrwV4q+f03iK35OKN368kqdE095E\nnT1A2D8TCPtnQna7maMbpiVcSI2NyPdci9Zuw91j6wg/skaj3cTBmdJthjxjVhaHBt2IDTlO+NG1\nnPmtT45jnly5lNv7UhguLBjMPd+NGm2B0x8fpO8+dDFFa2cV93R09ajYfzpnZvbm5s5F3Nz5xJMi\nFQpc+k7VeDpjRmIcN3Ys5MaOhVrXVSh1qdh/OrpGuVuV874IPB+s8RS/J33Ws2v291/07s6uA0cY\nMWEKIyZMyW6v6lYJV5dy3IvI/+2bndp8wIr1W1i+VvP9U8HZidGDv3ju3N7dOnDkhB//rN1Ex74D\ncxzzWc9u2d/n9r4UBr/TZ7KfXPikpg3q8svEMYWQkRBCCPFm0HnxECGEEKLw1Zq0g5LN+2FSvDw6\n+obomVljWb4WlT6fSflek7LH2VZvhdugeZiWrISOviEGlnYUb9qb6t+sR0dPv0BytSxXm2pjVmJe\ntlp2rg6NelDj280aBZkcKRRU+nI2lYcswtq1IXomFujo6mFU1BHbGq2oMuLv7FVZkPv78jawcW9C\njQkbsXb1RGloitLAGMsKdag2ZjV2ddppjC3dbhgV+v6MZYU66JlZo1DqYWhTHPv6nak9aafWeAE+\nO7wY1K8nFcs7Y2RoSBFrK+rVqs6imT9qPIWw7QfN+GfeDCpXrICRoSH2drZ80bs7u9ctw0C/YD5D\n9WpVx3vlYmpVc8/Otd8nXTiwacULVxcpFAqWzJrKqoWzaOZZDysLc/T19CjjWJJ2rZqzfulcmnk+\nXrWX2/tSGDYtX0iHD1tQtIgNJsZGVHd3ZfZPE9m2aglGhtrn/AkhhBDvC4Va/bxnYQshhBCvRqFQ\nUHnIoveiuBB17iCB03tQvudkSrV6/uoR8XaJOLmV83MHkF9/bfLy8qJ79+7P3Cr2vth98Chte37B\njEnfMOyLvoWdjsiDTwaMQMfQDC8vr8JORQghxLtrnaRQt8IAACAASURBVKzMEkIIIYQQQgg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itHXzmdldW/d3c0Gg1On0zi1LkLxL2M50HwY/5v1nc5Xu43ynkA8Qmv6DloFH8d8yA8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ooaBq4dsBmyEGWt0pfthyfWkHBkWblLNvvrkYQsrRp1SDi6gpy4YNTN7ak37zQAosJ8Hp/5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\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3v+mLWdvB6DdqjYapHdLiQtJDzlOU/VTm7rKychVRG/lNlNVLy9oFgPgTa9Fz9ES3YUsK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\nhBBCtBoymCWEEEIIIYQQQgghWo1/A3SrEepRlTxgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "image/png": {
       "height": 300,
       "width": 500
      },
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interpretability\n",
    "dot_data = StringIO()\n",
    "export_graphviz(dtree, out_file=dot_data, \n",
    "                feature_names=list(train_df.drop(['survived'], axis=1)), \n",
    "                class_names = ['died', 'survived'],\n",
    "                rounded = True, filled= True, special_characters=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png(), width=500, height=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 509
    },
    "colab_type": "code",
    "id": "tMK_w0Sqa6sE",
    "outputId": "06bdc3c4-9f77-4b5d-baf7-044f31b1578c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAyhhAEAMIQSBgDAEEoYAABDKGEAAAyhhAEAMIQSBgDAEEoYAABDKGEAAAyhhAEAMIQS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\nYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7e15ed780>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex - 0.615\n",
      "age - 0.174\n",
      "fare - 0.147\n",
      "embarked - 0.035\n",
      "sibsp - 0.030\n",
      "parch - 0.000\n",
      "pclass - 0.000\n"
     ]
    }
   ],
   "source": [
    "# Feature importances\n",
    "features = list(X_test.columns)\n",
    "importances = dtree.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "num_features = len(importances)\n",
    "\n",
    "# Plot the feature importances of the tree\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(num_features), importances[indices], color=\"g\", align=\"center\")\n",
    "plt.xticks(range(num_features), [features[i] for i in indices], rotation='45')\n",
    "plt.xlim([-1, num_features])\n",
    "plt.show()\n",
    "\n",
    "# Print values\n",
    "for i in indices:\n",
    "    print (\"{0} - {1:.3f}\".format(features[i], importances[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hS_763WyD4_3"
   },
   "source": [
    "# Random forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sZ5P_xbMD6eb"
   },
   "source": [
    "A group, or ensemble, of decision trees together create a random forest. The idea is that a group of different trees will yield more accurate predictions compared to a single decision tree. But how can we have different trees if they're all made using the same data and optimized on a metric like IG? The trick here is that the different decision trees in the random forest are made of different subsets of data and even different thresholds for features. \n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/GokuMohandas/practicalAI/master/images/forest.png\" width=600>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DUEa6BJ5GHLF"
   },
   "source": [
    "# Scikit-learn implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ogq6dncjeb1U"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2nuRnhYKF-qT"
   },
   "outputs": [],
   "source": [
    "# Initialize Random forest\n",
    "forest = RandomForestClassifier(\n",
    "    n_estimators=args.n_estimators, criterion=\"entropy\", \n",
    "    max_depth=args.max_depth, min_samples_leaf=args.min_samples_leaf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "iSkEBA4Xe4gY",
    "outputId": "5a5766d7-2faa-4f6e-fc21-c40e86dc4563"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=4, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=5, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train\n",
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8IkBGrQ6fD11"
   },
   "outputs": [],
   "source": [
    "# Predictions\n",
    "pred_train = forest.predict(X_train)\n",
    "pred_test = forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "EMk8RWRSfD6C",
    "outputId": "54098b2a-6f35-4b76-b407-25cd5807d942"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train acc: 0.80, test acc: 0.68\n",
      "precision: 0.65. recall: 0.87, F1: 0.75\n"
     ]
    }
   ],
   "source": [
    "# Accuracy\n",
    "train_acc = accuracy_score(y_train, pred_train)\n",
    "test_acc = accuracy_score(y_test, pred_test)\n",
    "print (\"train acc: {0:.2f}, test acc: {1:.2f}\".format(train_acc, test_acc))\n",
    "\n",
    "# Calculate other evaluation metrics \n",
    "precision, recall, F1, _ = precision_recall_fscore_support(y_test, pred_test, average=\"binary\")\n",
    "print (\"precision: {0:.2f}. recall: {1:.2f}, F1: {2:.2f}\".format(precision, recall, F1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gYcrpjEvFEkF"
   },
   "source": [
    "# Interpretability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BtSEnVtYFEmj"
   },
   "source": [
    "It's very easy to inspect random forests and derive feature importance values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 509
    },
    "colab_type": "code",
    "id": "4zmqPwuZfD_3",
    "outputId": "024b95e1-a02c-4d22-a1bf-7e6a924e5417"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HsrOzlZKSoqysLE2ePFkpKSmX3Gfu3Llq1qxZnYUEAMAdVVvCGRkZio2NlSSFh4erqKhI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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff7e1598cc0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex - 0.503\n",
      "age - 0.198\n",
      "fare - 0.129\n",
      "embarked - 0.086\n",
      "parch - 0.037\n",
      "sibsp - 0.031\n",
      "pclass - 0.017\n"
     ]
    }
   ],
   "source": [
    "# Feature importances\n",
    "features = list(X_test.columns)\n",
    "importances = forest.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in forest.estimators_], axis=0)\n",
    "indices = np.argsort(importances)[::-1]\n",
    "num_features = len(importances)\n",
    "\n",
    "# Plot the feature importances of the tree\n",
    "plt.figure()\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(num_features), importances[indices], yerr=std[indices], \n",
    "        color=\"g\", align=\"center\")\n",
    "plt.xticks(range(num_features), [features[i] for i in indices], rotation='45')\n",
    "plt.xlim([-1, num_features])\n",
    "plt.show()\n",
    "\n",
    "# Print values\n",
    "for i in indices:\n",
    "    print (\"{0} - {1:.3f}\".format(features[i], importances[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q2VCgsSBK9rr"
   },
   "source": [
    "# Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zajR4pDlK9uf"
   },
   "source": [
    "In our random forest, we have many different hyperparameters (criterion, max_depth, etc.) and many of the models we will see in future lessons will have even more parameters. How will we know what values to pick? We have to tune the values based on the performance they yield on the validation set. Scikit learn offers functions to do exhaustive grid search so that we can tune our hyperparameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4RMeG5C3gGPg"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PmXiTM8PLKzT"
   },
   "outputs": [],
   "source": [
    "# Create the parameter grid \n",
    "param_grid = {\n",
    "    'bootstrap': [True],\n",
    "    'max_depth': [10, 20, 50],\n",
    "    'max_features': [len(features)],\n",
    "    'min_samples_leaf': [3, 4, 5],\n",
    "    'min_samples_split': [4, 8],\n",
    "    'n_estimators': [5, 10, 50] # of trees\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aIhJ4Wupgmvy"
   },
   "outputs": [],
   "source": [
    "# Initialize random forest\n",
    "forest = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cM6AE1Kigmyl"
   },
   "outputs": [],
   "source": [
    "# Instantiate grid search\n",
    "grid_search = GridSearchCV(estimator=forest, param_grid=param_grid, cv=3, \n",
    "                           n_jobs=-1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "colab_type": "code",
    "id": "NorCjCFSg3uU",
    "outputId": "2c8bd43f-7c28-4847-f72a-b46e6f493306"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 162 out of 162 | elapsed:    3.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'bootstrap': [True], 'max_depth': [10, 20, 50], 'max_features': [7], 'min_samples_leaf': [3, 4, 5], 'min_samples_split': [4, 8], 'n_estimators': [5, 10, 50]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit grid search to the data\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "NLPPr568g3xA",
    "outputId": "83989349-eb37-4988-e5bc-0e4213523209"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'max_depth': 10,\n",
       " 'max_features': 7,\n",
       " 'min_samples_leaf': 3,\n",
       " 'min_samples_split': 8,\n",
       " 'n_estimators': 10}"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the best combination of parameters\n",
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "SAA7pZamg3zp",
    "outputId": "08d9fef5-82e2-4cc5-8836-176a59610fed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=10, max_features=7, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=3, min_samples_split=8,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train using best parameters\n",
    "best_forest = grid_search.best_estimator_\n",
    "best_forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FfrJFo6TiEBJ"
   },
   "outputs": [],
   "source": [
    "# Predictions\n",
    "pred_train = best_forest.predict(X_train)\n",
    "pred_test = best_forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "1VM5goioiEjQ",
    "outputId": "4d25a809-a8d9-415d-ce82-e701364cfd67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train acc: 0.89, test acc: 0.70\n",
      "precision: 0.70. recall: 0.79, F1: 0.75\n"
     ]
    }
   ],
   "source": [
    "# Accuracy\n",
    "train_acc = accuracy_score(y_train, pred_train)\n",
    "test_acc = accuracy_score(y_test, pred_test)\n",
    "print (\"train acc: {0:.2f}, test acc: {1:.2f}\".format(train_acc, test_acc))\n",
    "\n",
    "# Calculate other evaluation metrics \n",
    "precision, recall, F1, _ = precision_recall_fscore_support(y_test, pred_test, average=\"binary\")\n",
    "print (\"precision: {0:.2f}. recall: {1:.2f}, F1: {2:.2f}\".format(precision, recall, F1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L0aQUomQoni1"
   },
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iF1iNNhxLIpE"
   },
   "source": [
    "- regression example\n",
    "- gini vs. entropy"
   ]
  }
 ],
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   "name": "06_Random_Forests",
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  "kernelspec": {
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